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A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm.

Takumi Nakane¹, Xuequan Lu², Chao Zhang¹

¹University of Fukui, Fukui, Japan.

Computational Intelligence and Neuroscience

|October 16, 2020

Summary

This summary is machine-generated.

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This study introduces a novel Search History Crossover (SHX) model for real-coded genetic algorithms (RCGA). SHX enhances offspring generation by leveraging past search data, improving accuracy and convergence speed with minimal computational overhead.

Area of Science:

Evolutionary Computation
Artificial Intelligence
Optimization Algorithms

Background:

Genetic algorithms generate offspring iteratively, creating valuable search history.
Real-coded genetic algorithms (RCGA) can benefit from improved offspring generation strategies.

Purpose of the Study:

To propose and evaluate a novel crossover model, Search History Crossover (SHX), for RCGA.
To enhance RCGA performance by exploiting cached search history in an online manner.
To develop a data-driven method for offspring selection that requires no additional fitness evaluations.

Main Methods:

Survivor individuals from past generations are archived to form the search history.
The search history is clustered, and each cluster is assigned a score.

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Optogenetic Random Mutagenesis Using Histone-miniSOG in C. elegans

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A crossover model (SHX) is introduced, driven by this scored search history for offspring selection.

Main Results:

SHX significantly enhances the performance of RCGA on 15 benchmark functions.
Improvements were observed in both solution accuracy and convergence speed.
The additional runtime introduced by SHX is negligible compared to the overall processing time.

Conclusions:

The proposed SHX model effectively leverages search history to improve RCGA performance.
SHX is particularly beneficial for optimization tasks with limited budgets or expensive fitness evaluations.
SHX offers a computationally efficient method for boosting evolutionary algorithm performance.