Evolutionary Demographic Algorithms

TL;DR

This paper presents a distributed Java-JINI library for genetic algorithms that uses sub-populations across networked computers, enhancing diversity and evaluation capacity compared to traditional methods.

Contribution

It introduces a novel distributed genetic algorithm framework with configurable sub-populations, migration policies, and network topologies, improving diversity and scalability.

Findings

Delays convergence, maintaining higher genetic diversity.

Enables more evaluations through distributed computing.

Improves scalability over traditional genetic algorithms.

Abstract

Most of the problems in genetic algorithms are very complex and demand a large amount of resources that current technology can not offer. Our purpose was to develop a Java-JINI distributed library that implements Genetic Algorithms with sub-populations (coarse grain) and a graphical interface in order to configure and follow the evolution of the search. The sub-populations are simulated/evaluated in personal computers connected trough a network, keeping in mind different models of sub-populations, migration policies and network topologies. We show that this model delays the convergence of the population keeping a higher level of genetic diversity and allows a much greater number of evaluations since they are distributed among several computers compared with the traditional Genetic Algorithms.