A theory of the phenomenology of multipopulation genetic algorithm with an application to the Ising model
This work addresses the problem of resource waste in MPGA parameter tuning for researchers in optimization and statistical mechanics, but it is incremental as it builds on existing MPGA and statistical mechanics concepts.
The paper tackled the challenge of predicting solution quality evolution in multipopulation genetic algorithms (MPGA) by deriving equations from statistical mechanics to model dynamics related to connectivity. As a result, it demonstrated MPGA as an efficient alternative to the thermalization phase of the Metropolis-Hastings algorithm for the Ising model, though no concrete numerical improvements were provided.
Genetic algorithm (GA) is a stochastic metaheuristic process consisting on the evolution of a population of candidate solutions for a given optimization problem. By extension, multipopulation genetic algorithm (MPGA) aims for efficiency by evolving many populations, or islands, in parallel and performing migrations between them periodically. The connectivity between islands constrains the directions of migration and characterizes MPGA as a dynamic process over a network. As such, predicting the evolution of the quality of the solutions is a difficult challenge, implying in the waste of computer resources and energy when the parameters are inadequate. By using models derived from statistical mechanics, this work aims to estimate equations for the study of dynamics in relation to the connectivity in MPGA. To illustrate the importance of understanding MPGA, we show its application as an efficient alternative to the thermalization phase of Metropolis-Hastings algorithm applied to the Ising model.