Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State

Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State

We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine learning (ML) models, focusing on dynamic adaptation to the evolutionary state. We compare different methods for (1) switching between actual and approximate fitness, (2) sampling the population...

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Bibliographic Details
Main Authors: Itai Tzruia, Tomer Halperin, Moshe Sipper, Achiya Elyasaf
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Information
Subjects:
genetic algorithm
machine learning
fitness approximation
surrogate-assisted evolutionary algorithm
regression
agent simulation
Online Access:https://www.mdpi.com/2078-2489/15/12/744
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