Tracking multiple dynamic targets using a network of sensors is a challenging yet essential task in intelligent vehicles, that requires positioning the sensors in optimal locations to collect informative measurements ...
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We analyze the energy consumption of running evolutionary algorithms in batch as a function of the rest time between runs. It is shown that energy consumption can be reduced by 5%-8% by inserting short pauses between ...
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The design of chemical products requires the optimization of desired properties in molecular structures. Traditional techniques are based on laboratory experimentation and are hindered by the intractable number of alt...
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In this study, we propose a novel method to enhance the interpretability of genetic Programming Hyper-Heuristics (GPHH) by employing counterfactual explanations for genetic Programming (GP) evolved rules in dynamic st...
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Multi-label datasets often possess hundreds of irrelevant or redundant features that can negatively affect classification performance over multiple co-occuring class labels, necessitating feature selection. Sparsity-b...
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Cartesian genetic Programming (CGP) allows for the optimization of interpretable function representations. However, comprehending the vast and combinatorially complex search space inherent to CGP remains challenging, ...
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In evolutionarycomputation, it is commonly assumed that a search algorithm acquires knowledge about a problem instance by sampling solutions from the search space and evaluating them with a fitness function. This is ...
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This 2-page extended abstract gives a short overview of the Dyn-Stack competition, which has been hosted at the genetic and evolutionarycomputationconference (GECCO) since 2020. The challenge is to generate optimize...
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Hyperparameter optimization is a crucial problem in evolutionarycomputation. In fact, the values of the hyperparameters directly impact the trajectory taken by the optimization process, and their choice requires exte...
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