A Memory Optimized Data Structure for Binary Chromosomes in Genetic Algorithm
This work addresses memory optimization for binary genotypes in Genetic Algorithms, which is an incremental improvement for computational efficiency in evolutionary computing.
The paper tackles the memory inefficiency of binary chromosomes in Genetic Algorithms by proposing a memory-optimized metadata-based data structure, which improves memory utilization and allele retention capacity, supported by mathematical proof.
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.