ECTA 2023 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 25
Title:

Real-World Optimization Benchmark from Vehicle Dynamics: Specification of Problems in 2D and Methodology for Transferring (Meta-)Optimized Algorithm Parameters
Authors:

André Thomaser, Marc-Eric Vogt, Thomas Bäck and Anna V. Kononova
Abstract: The algorithm selection problem is of paramount importance in achieving high-quality results while minimizing computational effort, especially when dealing with expensive black-box optimization problems. In this paper, we address this challenge by using randomly generated artificial functions that mimic the landscape characteristics of the original problem while being inexpensive to evaluate. The similarity between the artificial function and the original problem is quantified using Exploratory Landscape Analysis. We demonstrate a significant performance improvement on five real-world vehicle dynamics problems by transferring the parameters of the Covariance Matrix Adaptation Evolution Strategy tuned to these artificial functions. We provide a complete set of simulated values of braking distance for fully enumerated 2D design spaces of all five real-world optimization problems. So, replication of our results and benchmarking directly on the real-world problems is possible. Beyond the scope of this paper, this data can be used as a benchmarking set for multi-objective optimization with up to five objectives.
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Paper Nr: 29
Title:

Shaping the Behavior Space with Counterfactual Agents in Multi-Objective Map Elites
Authors:

Anna Nickelson, Nicholas Zerbel, Gaurav Dixit and Kagan Tumer
Abstract: Success in many real-world problems cannot be adequately defined under a single objective and instead requires multiple, sometimes competing, objectives to define the problem. To perform well in these environ-ments, autonomous agents need to have a variety of skills and behaviors to balance these objectives. The combination of Multi-Objective Optimization (MOO) and Quality Diversity (QD) methods, such as in MultiObjective Map Elites (MOME), aim to provide a set of policies with diverse behaviors that cover multiple objectives. However, MOME is unable to diversify its search across the behavior space, resulting in significantly reduced coverage of the global Pareto front. This paper introduces Counterfactual Behavior Shaping for Multi-Objective Map Elites (C-MOME), a method that superimposes counterfactual agents onto the state space of a learning agent to more richly define the diversity of agent behaviors. Counterfactuals explicitly introduce new forms of diversity in agent behaviors, resulting in C-MOME’s effective coverage of behavioral niches; this provides a broader set of Pareto optimal policies. We show that C-MOME covers more than twice as much of the behavior space compared to MOME while increasing the hypervolume of the global Pareto front by over 40%.
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Paper Nr: 32
Title:

A One-Vs-One Approach to Improve Tangled Program Graph Performance on Classification Tasks
Authors:

Thibaut Bellanger, Matthieu L. Berre, Manuel Clergue and Jin-Kao Hao
Abstract: We propose an approach to improve the classification performance of the Tangled Programs Graph (TPG). TPG is a genetic programming method that aims to discover Directed Acyclic Graphs (DAGs) through an evolutionary process, where the edges carry programs that allow nodes to create a route from the root to a leaf, and the leaves represent actions or labels in classification. Despite notable successes in reinforcement learning tasks, TPG’s performance in classification appears to be limited in its basic version, as evidenced by the scores obtained on the MNIST dataset. However, the advantage of TPG compared to neural networks is to obtain, like decision trees, a global decision that is decomposable into simple atomic decisions and thus more easily explainable. Compared to decision trees, TPG has the advantage that atomic decisions benefit from the expressiveness of a pseudo register-based programming language, and the graph evolutionary construction prevents the emergence of overfitting. Our approach consists of decomposing the multi-class problem into a set of one-vs-one binary problems, training a set of TPG for each of them, and then combining the results of the TPGs to obtain a global decision, after selecting the best ones by a genetic algorithm. We test our approach on several benchmark datasets, and the results obtained are promising and tend to validate the proposed method.
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Paper Nr: 33
Title:

Equidistant Reorder Operator for Cartesian Genetic Programming
Authors:

Henning Cui, Andreas Margraf and Jörg Hähner
Abstract: The Reorder operator, an extension to Cartesian Genetic Programming (CGP), eliminates limitations of the classic CGP algorithm by shuffling the genome. One of those limitations is the positional bias, a phenomenon in which mostly genes at the start of the genome contribute to an output, while genes at the end rarely do. This can lead to worse fitness or more training iterations needed to find a solution. To combat this problem, the existing Reorder operator shuffles the genome without changing its phenotypical encoding. However, we argue that Reorder may not fully eliminate the positional bias but only weaken its effects. By introducing a novel operator we name Equidistant-Reorder, we try to fully avoid the positional bias. Instead of shuffling the genome, active nodes are reordered equidistantly in the genome. Via this operator, we can show empirically on four Boolean benchmarks that the number of iterations needed until a solution is found decreases; and fewer nodes are needed to efficiently find a solution, which potentially saves CPU time with each iteration. At last, we visually analyse the distribution of active nodes in the genomes. A potential decrease of the negative effects of the positional bias can be derived with our extension.
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Paper Nr: 38
Title:

MOEA/D with Adaptive Mutation Operator Based on Walsh Decomposition: Application to Nuclear Reactor Control Optimization
Authors:

Baptiste Gasse, Sébastien Verel and Jean-Michel Do
Abstract: France has a fleet of nuclear reactors that makes up a significant proportion of the electricity generation mix. This over-representation of nuclear power compared with other energy sources leads reactors to operate in load following mode in order to balance supply and demand on the electricity grid. The increasing penetration of intermittent energies in the mix and the desire not to renew the entire current nuclear fleet bring active research into optimising the control of reactors operating in load following mode to allow them greater flexibility. In this study, we propose to solve a new bi-objective unit commitment problem using an MOEA/D algorithm equipped with an adaptive mutation operator based on a Walsh surrogate model of a black-box function with a high computation cost. The method consists of taking advantage of the linear effects associated with the problem variables thanks to the Walsh coefficients to regularly update the mutation rate of the variation operator and explore the problem’s search space more judiciously. We show that this method enables to penalize some variables by decreasing their mutation probability without affecting the global performance of the search for Pareto-optimal solutions, which makes it similar to an adaptive in-line fitness landscape analysis.
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Paper Nr: 43
Title:

Enhancing ε-Sampling in the AεSεH Evolutionary Multi-Objective Optimization Algorithm
Authors:

Yu Takei, Hernán Aguirre and Kiyoshi Tanaka
Abstract: AεSεH is one of the evolutionary algorithms used for many-objective optimization. It uses ε-dominance during survival selection to sample from a large set of non-dominated solutions to reduce it to the required population size. The sampling mechanism works to suggest a subset of well distributed solutions, which boost the performance of the algorithm in many-objective problems compared to Pareto dominance based multi-objective algorithms. However, the sampling mechanism does not select exactly the target number of individuals given by the population size and includes a random selection component when the size of the sample needs to be adjusted. In this work, we propose a more elaborated method also based on ε-dominance to reduce randomness and obtain a better distributed sample in objective-space to further improve the performance of the algorithm. We use binary MNK-landscapes to study the proposed method and show that it significantly increases the performance of the algorithm on non-linear problems as we increase the dimensionality of the objective space and decision space.
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Paper Nr: 56
Title:

On Switching Selection Methods to Increase Parsimony Pressure
Authors:

Allan de Lima, Samuel Carvalho, Douglas M. Dias, Joseph P. Sullivan and Conor Ryan
Abstract: We proposed a novel and simple selection system that alternates between tournament and Lexicase selection to tackle the bloat issue. In this way, we used Lexi2 , an implementation of Lexicase with lexicographic parsimony pressure, adopting the number of nodes in our solutions as size measurement. In addition, we increased the parsimony pressure by adding a penalty, also based on the number of nodes, to the aggregated fitness score. We analysed different scenarios, including some without extra parameters, in five benchmark problems: 2-bit Multiplier, 5-bit Parity, Car Evaluation, LED and Heart Disease. We succeeded in all of them in at least one scenario, reducing the size significantly while maintaining fitness. Beyond error and size, we also included results for the average number of fitness cases used in each generation.
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Paper Nr: 63
Title:

Assisting Convergence Behaviour Characterisation with Unsupervised Clustering
Authors:

Helena Stegherr, Michael Heider and Jörg Hähner
Abstract: Analysing the behaviour of metaheuristics comprehensively and thereby enhancing explainability requires large empirical studies. However, the amount of data gathered in such experiments is often too large to be examined and evaluated visually. This necessitates establishing more efficient analysis procedures, but care has to be taken so that these do not obscure important information. This paper examines the suitability of clustering methods to assist in the characterisation of the behaviour of metaheuristics. The convergence behaviour is used as an example as its empirical analysis often requires looking at convergence curve plots, which is extremely tedious for large algorithmic datasets. We used the well-known K-Means clustering method and examined the results for different cluster sizes. Furthermore, we evaluated the clusters with respect to the characteristics they utilise and compared those with characteristics applied when a researcher inspects convergence curve plots. We found that clustering is a suitable technique to assist in the analysis of convergence behaviour, as the clusters strongly correspond to the grouping that would be done by a researcher, though the procedure still requires background knowledge to determine an adequate number of clusters. Overall, this enables us to inspect only few curves per cluster instead of all individual curves.
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Paper Nr: 66
Title:

Challenges of ELA-Guided Function Evolution Using Genetic Programming
Authors:

Fu Xing Long, Diederick Vermetten, Anna V. Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck and Niki van Stein
Abstract: Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years. Within topics such as instance space analysis (ISA), the generation of new problems can provide new benchmarks which are not yet explored in existing research. Beyond that, this function generation can also be exploited for solving expensive real-world optimization problems. By generating fast-to-evaluate functions with similar optimization properties to the target problems, we can create a test set for algorithm selection and configuration purposes. However, the generation of functions with specific target properties remains challenging. While features exist to capture low-level landscape properties, they might not always capture the intended high-level features. We show that it is challenging to find satisfying functions through a genetic programming (GP) approach guided by the exploratory landscape analysis (ELA) properties. Our results suggest that careful considerations of the weighting of ELA properties, as well as the distance measure used, might be required to evolve functions that are sufficiently representative to the target landscape.
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Paper Nr: 81
Title:

Can HP-protein Folding Be Solved with Genetic Algorithms? Maybe not
Authors:

Reitze Jansen, Ruben Horn, Okke van Eck, Kristian Verduin, Sarah L. Thomson and Daan van den Berg
Abstract: Genetic algorithms might not be able to solve the HP-protein folding problem because creating random individuals for an initial population is very hard, if not impossible. The reason for this, is that the expected number of constraint violations increases with instance size when randomly sampling individuals, as we will show in an experiment. Thereby, the probability of randomly sampling a valid individual decreases exponentially with instance size. This immediately prohibits resampling, and repair mechanisms might also be non-applicable. Backtracking could generate a valid random individual, but it runs in exponential time, and is therefore also unsuitable. No wonder that previous approaches do not report how (often) random samples are created, and only address small instances. We contrast our findings with TSP, which is also NP-hard, but does not have these problems.
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Short Papers
Paper Nr: 15
Title:

The Partition Problem, and How The Distribution of Input Bits Affects the Solving Process
Authors:

Nikita Sazhinov, Ruben Horn, Pieter Adriaans and Daan van den Berg
Abstract: The hardness of the partition problem does not only depend on the number of integers in the problem instance, but also on their magnitude, measured in informational bits. In this work, we will show that also the exact distribution of informational bits among the integers influences the hardness for at least one exact and three heuristic algorithms.
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Paper Nr: 19
Title:

Interactive Role Mining Including Expert Knowledge into Evolutionary Algorithms
Authors:

Simon Anderer, Nicolas Justen, Bernd Scheuermann and Sanaz Mostaghim
Abstract: To protect the security of corporate IT systems against erroneous or fraudulent behavior of employees, Role Based Access Control has proven to be an effective concept. The corresponding NP-complete Role Mining Problem aims at finding a minimal set of roles and an assignment of roles to users. A valuable source of additional information, which is not yet included in current role mining algorithms, is expert knowledge. Users of role mining software should be enabled to monitor the role mining process and interactively transfer their knowledge to the system, for example by suggesting good or deleting bad roles. This leads to dynamically occurring interaction events, which must be included into the optimization process preferably in real-time, since these have the potential to accelerate the optimization process or improve the solution quality. This paper introduces to interactive role mining. For this purpose, the hitherto static RMP is considered as dynamic optimization problem. Since evolutionary algorithms have shown to be a promising solution approach, it is shown how events emerging from user interaction can be integrated. The integration of different interaction events into the evolutionary algorithm and their impact on the optimization process are then evaluated in a range of experiments.
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Paper Nr: 23
Title:

A Study on Multi-Objective Optimization of Epistatic Binary Problems Using Q-learning
Authors:

Yudai Tagawa, Hernán Aguirre and Kiyoshi Tanaka
Abstract: In this paper, we study distributed and centralized approaches of Q-learning for multi-objective optimization of binary problems and investigate their characteristics and performance on complex epistatic problems using MNK-landscapes. In the distributed approach an agent receives its reward optimizing one of the objective functions and collaborates with others to generate Pareto non-dominated solutions. In the centralized approach the agent receives its reward based on Pareto dominance optimizing simultaneously all objective functions. We encode a solution as part of a state and investigate two types of actions as one-bit mutation operators, two methods to generate an episode’s initial state and the number of steps an agent is allowed to explore without improving. We also compare with some evolutionary multi-objective optimizers showing that Q-learning based approaches scale up better as we increase the number of objectives on problems with large epistasis.
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Paper Nr: 30
Title:

A Genetic Algorithm for Marine Spatial Planning with Minimized Conflict Between Planned Regions
Authors:

Seo-Ah Yu, Choong-Ki Kim and Yong-Hyuk Kim
Abstract: To efficiently utilize marine space, numerous experiments have been conducted to optimize marine space. We utilize a genetic algorithm (GA) to develop an optimal spatial plan for the Exclusive Economic Zone (EEZ). The space can be allocated for six different uses, each with its own weight. Conflicts exist among these uses. The objective is to maximize the fitness of the space by evaluating it at the cell level. This involves maximizing the evaluation score, which is determined by the weighted sum of each cell’s use, minimizing conflicts, and reducing the number of clusters to ensure continuity of use. The basic allocation model, which achieves the best quality among random solutions within the same running time as our GA, is used for comparison. Experimental results showed that, when our method is compared to the basic model, the evaluation scores increased by approximately 20%, except for one case of use ‘ecology’. Additionally, conflicts between zones decreased, and the total fitness improved as the number of clusters decreased.
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Paper Nr: 31
Title:

Comparison of Different Surrogate Models for the JADE Algorithm
Authors:

Konrad Krawczyk and Jarosław Arabas
Abstract: We investigate the performance of various regression-based surrogate models integrated with a ranking procedure in the Adaptive Differential Evolution with an Optional External Archive (JADE) method. We perform regression of the fitness function by the surrogate model to reduce the number of fitness evaluations needed to achieve the optimization progress. The surrogate model training process should be relatively cheap since training is performed many times along with the optimization process. Therefore we investigate surrogate models based on k Nearest Neighbors, Random Forests, and Support Vector Machines. We test the effectiveness of JADE with and without the surrogate models using the CEC2013 benchmark set for single-criterion continuous optimization. Experimental data confirm the benefits of using the surrogate models and indicate the difference in efficiency improvement between the considered models.
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Paper Nr: 35
Title:

Adaptive Case Selection for Symbolic Regression in Grammatical Evolution
Authors:

Krishn K. Gupt, Meghana Kshirsagar, Douglas M. Dias, Joseph P. Sullivan and Conor Ryan
Abstract: The analysis of time efficiency and solution size has recently gained huge interest among researchers of Grammatical Evolution (GE). The voluminous data have led to slower learning of GE in finding innovative solutions to complex problems. Few works incorporate machine learning techniques to extract samples from big datasets. Most of the work in the field focuses on optimizing the GE hyperparameters. This leads to the motivation of our work, Adaptive Case Selection (ACS), a diversity-preserving test case selection method that adaptively selects test cases during the evolutionary process of GE. We used six symbolic regression synthetic datasets with diverse features and samples in the preliminary experimentation and trained the models using GE. Statistical Validation of results demonstrates ACS enhancing the efficiency of the evolutionary process. ACS achieved higher accuracy on all six problems when compared to conventional ‘train/test split.’ It outperforms four out of six problems against the recently proposed Distance-Based Selection (DBS) method while competitive on the remaining two. ACS accelerated the evolutionary process by a factor of 14X and 11X against both methods, respectively, and resulted in simpler solutions. These findings suggest ACS can potentially speed up the evolutionary process of GE when solving complex problems.
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Paper Nr: 36
Title:

Comparative Analysis of Metaheuristics Techniques for Trade Data Harmonization
Authors:

Himadri S. Khargharia, Sid Shakya and Dymitr Ruta
Abstract: The harmonization of trade data from two datasets containing different and distinct categories poses a challenging real-world problem. To address this issue, we model it as an optimization problem and investigate the effectiveness of various metaheuristic techniques in achieving optimal or near-optimal solutions. Particularly, we analyze the performance of Genetic Algorithm (GA), Population-based Incremental Learning (PBIL), DEUM, and Simulated Annealing (SA) in terms of best fitness, scalability, and their respective strengths and weaknesses. We explore multiple instances of the trade data harmonisation problem of different sizes to assess the applicability of these techniques in mitigating trade volume disparities. By examining the outcomes, our research offers valuable insights into the suitability of metaheuristic techniques for this problem.
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Paper Nr: 40
Title:

Optimizing CMA-ES with CMA-ES
Authors:

André Thomaser, Marc-Eric Vogt, Thomas Bäck and Anna V. Kononova
Abstract: The performance of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is significantly affected by the selection of the specific CMA-ES variant and the parameter values used. Furthermore, optimal CMA-ES parameter configurations vary across different problem landscapes, making the task of tuning CMA-ES to a specific optimization problem a challenging mixed-integer optimization problem. In recent years, several advanced algorithms have been developed to address this problem, including the Sequential Model-based Algorithm Configuration (SMAC) and the Tree-structured Parzen Estimator (TPE). In this study, we propose a novel approach for tuning CMA-ES by leveraging CMA-ES itself. Therefore, we combine the modular CMA-ES implementation with the margin extension to handle mixed-integer optimization problems. We show that CMA-ES can not only compete with SMAC and TPE but also outperform them in terms of wall clock time.
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Paper Nr: 46
Title:

A Comparison of the State-of-the-Art Evolutionary Algorithms with Different Stopping Conditions
Authors:

Jana Herzog, Janez Brest and Borko Bošković
Abstract: This paper focuses on the comparison of the state-of-the-art algorithms and the influence of a stopping condition, the maximum number of function evaluations, on the optimization process. The main aim is to compare the chosen state-of-the-art algorithms with different predetermined stopping conditions and observe if they are comparable in reaching a certain quality of solution on a given set of benchmark functions. For this analysis, the four most recent state-of-the-art evolutionary algorithms were chosen for comparison on the latest set of benchmark functions. We utilized a fixed-budget approach with different values of stopping conditions. Small differences in the algorithms’ performances are observed and the obtained results are also statistically analyzed. Different values of the stopping conditions show different rankings of evolutionary algorithms without the significant difference. The possible reason for this is that their performances are very close.
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Paper Nr: 54
Title:

Comparative Evaluation of Metaheuristic Algorithms for Hyperparameter Selection in Short-Term Weather Forecasting
Authors:

Anuvab Sen, Arul R. Mazumder, Dibyarup Dutta, Udayon Sen, Pathikrit Syam and Sandipan Dhar
Abstract: Weather forecasting plays a vital role in numerous sectors, but accurately capturing the complex dynamics of weather systems remains a challenge for traditional statistical models. Apart from Auto Regressive time forecasting models like ARIMA, deep learning techniques (Vanilla ANNs, LSTM and GRU networks) have shown promise in improving forecasting accuracy by capturing temporal dependencies. This paper explores the application of metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO) to automate the search for optimal hyperparameters in these model architectures. Metaheuristic algorithms excel in global optimization, offering robustness, versatility, and scalability in handling non-linear problems. We present a comparative analysis of different model architectures integrated with metaheuristic optimization, evaluating their performance in weather forecasting based on metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The results demonstrate the potential of metaheuristic algorithms in enhancing weather forecasting accuracy & helps in determining the optimal set of hyper-parameters for each model. The paper underscores the importance of harnessing advanced optimization techniques to select the most suitable metaheuristic algorithm for the given weather forecasting task.
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Paper Nr: 59
Title:

A Game Theoretic Approach Based on Differential Evolution to Ensemble Learning for Classification
Authors:

Rodica I. Lung
Abstract: Aggregating results of several learners known to each perform well on different data types is a challenging task that requires finding intelligent, trade-off solutions. A simple game-theoretic approach to this problem is proposed. A non-cooperative game is used to aggregate the results of different classification methods. The Nash equilibrium of the game is approximated by using a Differential Evolution algorithm. Numerical experiments indicate the potential of the approach for a set of synthetic and real-world data.
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Paper Nr: 60
Title:

Hybrid Training to Generate Robust Behaviour for Swarm Robotics Tasks
Authors:

Pedro Romano, Luís Nunes and Sancho Oliveira
Abstract: Training of robotic swarms is usually done for a specific task and environment. The more specific the training is, the more the likelihood of reaching a good performance. Still, flexibility and robustness are essential for autonomy, enabling the robots to adapt to different environments. In this work, we study and compare approaches to robust training of a small simulated swarm on a task of cooperative identification of moving objects. Controllers are obtained via evolutionary methods. The main contribution is the test of the effectiveness of training in multiple environments: simplified versions of terrain, marine and aerial environments, as well as on ideal, noisy and hybrid (mixed environment) scenarios. Results show that controllers can be generated for each of these scenarios, but, contrary to expectations, hybrid evolution and noisy training do not, in general, generate better controllers for the different scenarios. Nevertheless, the hybrid controller reaches a performance level par with specialized controllers in several scenarios, and can be considered a more robust solution.
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Paper Nr: 62
Title:

Meta-Heuristic Optimization of Transistor Sizing in CMOS Digital Designs
Authors:

Prashanth H. C. and Madhav Rao
Abstract: Designing new custom standard cells or digital circuits using automated optimization is challenging considering the large design space, performance trade-offs and continuous technology progression. Besides, a comprehensive study and analysis of different algorithms applied towards optimizing higher-order custom digital circuit design is imperative. In this work, 28 Transistor (28T) 1-bit full-adder (FA) is designed and investigated for six optimization algorithms, including particle-swarm-optimization (PSO), evolutionary strategy (ES), genetic algorithm (GA), differential evolution (DE), NSGA-II, and NSGA-III. The algorithms are evaluated and benchmarked, considering diversity of candidate solutions, monotonicity of fitness convergence, and capability to reach the best solution when initiated with a randomly seeded solution. This work establishes that GA produces best-fit circuits among all the single-objective algorithms. ES and GA exhibit good designspace exploration, unlike PSO and DE, which are influenced by local optima. NSGA-II, and NSGA-III are preferred when the objective is to give equal importance to the targeted parameters. The extensive evaluation of the algorithms in this work will aid in adopting an effective strategy for optimizing custom circuits for the specified objective parameters.
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Paper Nr: 68
Title:

Filter Evolution Using Cartesian Genetic Programming for Time Series Anomaly Detection
Authors:

Andreas Margraf, Henning Cui, Stefan Baumann and Jörg Hähner
Abstract: Industrial monitoring relies on reliable and resilient systems to cope with unforeseen and changing environmental factors. The identification of critical conditions calls for efficient feature selection and algorithm configuration for accurate classification. Since the design process depends on experts who define parameters and develop classification models, it remains a time-consuming and error-prone task. In this paper, we suggest an evolutionary learning approach to create filter pipelines for machine health and condition monitoring. We apply a method called Cartesian Genetic Programming (CGP) to explore the search space and tune parameters for time series segmentation problems. CGP is a nature-inspired algorithm where nodes are aligned in a two-dimensional grid. Since programs generated by CGP are compact and short, we deem this concept efficient for filter evolution and parameter tuning to create performant classifiers. For better use of resources, we introduce a dependency graph to allow only valid combinations of filter operators during training. Furthermore, this novel concept is critically discussed from a efficiency and quality point of view as well as its effect on classifier accuracy on scarce data. Experimental results show promising results which - in combination with the novel concept - competes with state-of-the-art classifiers for problems of low and medium complexity. Finally, this paper poses research questions for future investigations and experimentation.
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Paper Nr: 74
Title:

Towards Understanding Crossover for Cartesian Genetic Programming
Authors:

Henning Cui, Andreas Margraf, Michael Heider and Jörg Hähner
Abstract: Unlike in traditional Genetic Programming, Cartesian Genetic Programming (CGP) does not commonly feature a recombination/crossover operator, although recombination plays an important role in other evolutionary techniques, including Genetic Programming from which CGP originates. Instead, CGP mainly depends on mutation and selection operators in their evolutionary search. To this day, it is still unclear as to why CGP’s performance does not generally improve with the addition of crossover. In this work, we argue that CGP’s positional bias might be a reason for this phenomenon. This bias describes a skewed distribution of active and inactive nodes, which might lead to destructive behaviour of standard recombination operators. We provide a first assessment with preliminary results. No final conclusion to this hypothesis can be drawn yet, as more thorough evaluations must be done first. However, our first results show promising trends and may lay the foundationf or future work.
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Paper Nr: 26
Title:

Maximizing Particle Coverage with Fixed-Area Rectangles
Authors:

Seung-Yeol Hong and Yong-Hyuk Kim
Abstract: In this study, our primary focus is on enhancing particle coverage through the effective deployment of rectangles with fixed areas and flexible shapes within a two-dimensional space. These rectangles represent areas optimally managed by various units like CCTV cameras, police personnel, search and rescue units and robots. Particles are objects designated for processing by these units. To realize this objective, we presented an efficient technique for deploying rectangles in a two-dimensional space using a genetic algorithm (GA). The GA searches for the optimal deployment of rectangles that maximizes the sum of the particle densities represented int the heat map. We experimented by applying our problem to maritime search and rescue planning. The main application of our method in maritime search and rescue planning is the deployment of search and rescue units in the ocean. As a result, the GA outperformed the greedy method by up to 14%. The experimental outcomes demonstrate the superiority of our proposed method compared to existing techniques. Specifically, its effectiveness becomes more pronounced when the total area covered by the placed rectangles is smaller than the entire search area.
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Paper Nr: 48
Title:

A Hybrid Bayesian-Genetic Algorithm Based Hyperparameter Optimization of a LSTM Network for Demand Forecasting of Retail Products
Authors:

Pravin Suryawanshi, Sandesh Gaikwad, Akansha Kumar, Akhil Patlolla and Sai K. Jayakumar
Abstract: Demand forecasting is highly influenced by the non-linearity of time series data. Deep neural networks such as long short-term memory networks (LSTM) are considered better forecasters of such data. However, the LSTM network’s performances are subject to hyperparameter values. This study proposes a hybrid approach to determine the optimal set of hyperparameters of an LSTM model using Bayesian optimization and genetic algorithm. Bayesian optimization explores the search space in the direction where the improvement over the existing solution is likely, based on a fitness function. At the same time, a genetic algorithm is an evolutionary approach that can achieve global convergence by using selection, crossover, and mutation operators. The proposed hybrid approach utilizes the strengths of both these algorithms to tune the values of the hyperparameter of the LSTM network to minimize the forecasting error. In the dataset considered, we found that the hybrid approach reduced the forecasting error by approximately 27% compared to the Bayesian optimization approach. Additionally, the proposed method is better than the genetic algorithm when performed independently, with a decrease in error value by approximately 13%.
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Paper Nr: 58
Title:

Too Constrained for Genetic Algorithms too Hard for Evolutionary Computing the Traveling Tournament Problem
Authors:

Kristian Verduin, Sarah L. Thomson and Daan van den Berg
Abstract: Unlike other NP-hard problems, the constraints on the traveling tournament problem are so pressing that it’s hardly possible to randomly generate a valid solution, for example, to use in a genetic algorithm’s initial population. In this study, we randomly generate solutions, assess the numbers of constraint violations, and extrapolate the results to predict the required number of samples for obtaining a single valid solution for any reasonable instance size. As it turns out, these numbers are astronomical, and we finish the study by discussing the feasibility of efficient sampling of valid solutions to various NP-hard problems.
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Paper Nr: 64
Title:

A Survey and Analysis of Evolutionary Operators for Permutations
Authors:

Vincent A. Cicirello
Abstract: There are many combinatorial optimization problems whose solutions are best represented by permutations. The classic traveling salesperson seeks an optimal ordering over a set of cities. Scheduling problems often seek optimal orderings of tasks or activities. Although some evolutionary approaches to such problems utilize the bit strings of a genetic algorithm, it is more common to directly represent solutions with permutations. Evolving permutations directly requires specialized evolutionary operators. Over the years, many crossover and mutation operators have been developed for solving permutation problems with evolutionary algorithms. In this paper, we survey the breadth of evolutionary operators for permutations. We implemented all of these in Chips-n-Salsa, an open source Java library for evolutionary computation. Finally, we empirically analyze the crossover operators on artificial fitness landscapes isolating different permutation features.
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Paper Nr: 83
Title:

Differential Evolution Algorithm Based Hyper-Parameters Selection of Convolutional Neural Network for Speech Command Recognition
Authors:

Sandipan Dhar, Anuvab Sen, Aritra Bandyopadhyay, Nanda D. Jana, Arjun Ghosh and Zahra Sarayloo
Abstract: Speech Command Recognition (SCR), which deals with identification of short uttered speech commands, is crucial for various applications, including IoT devices and assistive technology. Despite the promise shown by Convolutional Neural Networks (CNNs) in SCR tasks, their efficacy relies heavily on hyperparameter selection, which is typically laborious and time-consuming when done manually. This paper introduces a hyperparameter selection method for CNNs based on the Differential Evolution (DE) algorithm, aiming to enhance performance in SCR tasks. Training and testing with the Google Speech Command (GSC) dataset, the proposed approach showed effectiveness in classifying speech commands. Moreover, a comparative analysis with Genetic Algorithm-based selections and other deep CNN (DCNN) models highlighted the efficiency of the proposed DE algorithm in hyperparameter selection for CNNs in SCR tasks.
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