Using the Baldwin Effect to Accelerate a Genetic Algorithm
John R. Podlena, Tim Hendtlass · Industrial and Engineering Applications of Artificial Intelligence and Expert Systems · 2022
The standard Genetic Algorithm, originally inspired by natural evolution, has displayed its effectiveness in solving a wide variety of complex problems. This paper describes the use of the natural phenomenon known as the “Baldwin Effect” (or cross-generational learning) as an enhancement to the standard Genetic Algorithm. This is facilitated via the use of artificial neural networks to store aspects of the population’s history, and through the use of elitism in conjunction with a local heuristic. It also describes a method by which the negative side effects of a large elite sub-population can be counter-balanced by using an aging coefficient in the fitness calculation. The new algorithm is then tested on three standard genetic algorithm test functions, and on one commercial application of the genetic algorithm.