Multi-threaded Memory Efficient Crossover in C++ for Generational Genetic Programming
This work addresses memory constraints in evolutionary algorithms for researchers and practitioners dealing with large-scale genetic programming, though it appears incremental as it builds on existing generational approaches.
The paper tackles the problem of memory efficiency in genetic programming by introducing a multi-threaded crossover method in C++ that reduces memory usage to M + (2 times nthreads) simultaneously active individuals, enabling handling of large chromosomes or limited RAM.
C++ code snippets from a multi-core parallel memory-efficient crossover for genetic programming are given. They may be adapted for separate generation evolutionary algorithms where large chromosomes or small RAM require no more than M + (2 times nthreads) simultaneously active individuals.