A Memory Optimized Data Structure for Binary Chromosomes in Genetic Algorithm

TL;DR

This paper introduces a memory-efficient data structure for binary chromosomes in genetic algorithms, enhancing memory use and allele retention with proven mathematical validation.

Contribution

It proposes a novel memory-optimized implementation for binary genotypes in genetic algorithms, improving efficiency and capacity.

Findings

Enhanced memory utilization demonstrated

Increased allele retention capacity

Mathematical proof supports improvements

Abstract

This paper presents a memory-optimized metadata-based data structure for implementation of binary chromosome in Genetic Algorithm. In GA different types of genotypes are used depending on the problem domain. Among these, binary genotype is the most popular one for non-enumerated encoding owing to its representational and computational simplicity. This paper proposes a memory-optimized implementation approach of binary genotype. The approach improves the memory utilization as well as capacity of retaining alleles. Mathematical proof has been provided to establish the same.