Titlebook: Applications of Evolutionary Computation; 25th European Confer Juan Luis Jiménez Laredo,J. Ignacio Hidalgo,Kehind Conference proceedings 20

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https://doi.org/10.1007/978-1-60761-219-3 algorithm performance prediction. The experimental results point out that the selection of the supervised ML method is crucial, since different supervised ML regression models utilize the problem landscape features differently and there is no common pattern with regard to which landscape features are the most informative.

Discrete

Combining the Properties of Random Forest with Grammatical Evolution to Construct Ensemble Modelsandom Structured Grammatical Evolution as an adaptation of Random Forest to a symbolic regression problem. Using structured Grammatical Evolution, a set of weak predictors are built and combined on an ensemble model for prediction.

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Evolution of Acoustic Logic Gates in Granular Metamaterialsund that the latter were more evolvable. We believe this work may pave the way toward evolutionary design of increasingly sophisticated, programmable, and computationally dense metamaterials with certain advantages over more traditional computational substrates.

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Improving the Convergence and Diversity in Differential Evolution Through a Stock Market Criterion historical fitness and dimension-wise diversity is analyzed to determine if the DE continues operating normally or should diversify or intensify the search using additional operators. An exhaustive benchmark involving 37 optimization functions with different complexity levels confirmed the effectiveness of the proposed approach.

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Comparing Basin Hopping with Differential Evolution and Particle Swarm Optimizationll. The three methods perform well in general and the actual differences are related to the different groups of functions in the benchmark with Basin Hopping being the most robust technique, and Differential Evolution and Particle Swarm Optimization excelling on highly multi-modal functions.

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