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Bat Algorithm parameter settings analyzed using variance evolution theory

By PulseAugur Editorial · [2 sources] ·

This paper delves into the theoretical analysis of parameter settings for the Bat Algorithm, a type of evolutionary computation. Researchers demonstrate that applying dynamical systems theory and analyzing population variance evolution can yield effective parameter ranges. The findings from this theoretical approach are shown to align with results from numerical experiments, offering insights into the algorithm's exploration, exploitation, and convergence behaviors. AI

IMPACT Provides theoretical insights into evolutionary algorithms, potentially improving their performance and understanding.

RANK_REASON The cluster contains an academic paper detailing theoretical analysis of an algorithm.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Bat Algorithm parameter settings analyzed using variance evolution theory

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Xin-She Yang, Mehmet Karamanoglu ·

    Analysis of Parameter Settings for the Bat Algorithm Using Variance Evolution

    arXiv:2606.28644v1 Announce Type: cross Abstract: Parameter settings in evolutionary algorithms and metaheuristics are important because such parameter values can influence the performance of algorithms under evaluation. For a given algorithm, there are many different numerical e…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Mehmet Karamanoglu ·

    Analysis of Parameter Settings for the Bat Algorithm Using Variance Evolution

    Parameter settings in evolutionary algorithms and metaheuristics are important because such parameter values can influence the performance of algorithms under evaluation. For a given algorithm, there are many different numerical experiments to show that the algorithm can work wel…

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