Recovering from Population Extinction in the Animal Life Cycle Algorithm (ALCA)

Felix-Saul, J. C.; García-Valdéz, Mario

doi:10.1007/978-3-031-28999-6_26

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2024

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Cited by 2 publications

References 27 publications

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A New Breeding Crossover Approach for Evolutionary Algorithms

Felix-Saul,

García-Valdez

2024

Studies in Computational Intelligence

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A New Breeding Crossover Approach for Evolutionary Algorithms

Felix-Saul,

García-Valdez

2024

Studies in Computational Intelligence

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Extending Genetic Algorithms with Biological Life-Cycle Dynamics

Felix-Saul,

García-Valdez,

Merelo Guervós

et al. 2024

Biomimetics

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In this paper, we aim to enhance genetic algorithms (GAs) by integrating a dynamic model based on biological life cycles. This study addresses the challenge of maintaining diversity and adaptability in GAs by incorporating stages of birth, growth, reproduction, and death into the algorithm’s framework. We consider an asynchronous execution of life cycle stages to individuals in the population, ensuring a steady-state evolution that preserves high-quality solutions while maintaining diversity. Experimental results demonstrate that the proposed extension outperforms traditional GAs and is as good or better than other well-known and well established algorithms like PSO and EvoSpace in various benchmark problems, particularly regarding convergence speed and solution qu/ality. The study concludes that incorporating biological life-cycle dynamics into GAs enhances their robustness and efficiency, offering a promising direction for future research in evolutionary computation.

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Recovering from Population Extinction in the Animal Life Cycle Algorithm (ALCA)

Cited by 2 publications

References 27 publications

A New Breeding Crossover Approach for Evolutionary Algorithms

A New Breeding Crossover Approach for Evolutionary Algorithms

Extending Genetic Algorithms with Biological Life-Cycle Dynamics

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