Evolutionary approach to multi-objective problems using adaptive genetic algorithms

The paper describes an adaptive genetic algorithm used to achieve multi-objectives such as minimizing the territory losses and maximizing enemy air losses by finding the optimum distribution of aircraft fighting in a war scenario simulated by the THUNDER software. The adaptive genetic algorithm changes the mutation and crossover adaptively to provide fast convergence to the optimum possible solutions. According to the population of the fitness values obtained for each generation, three distribution properties (the mean, the variance and the best fitness value) are determined and used as input to a fuzzy-logic system for modifying the mutation and crossover rates to obtain the individuals of the next generation. This enables fast and smooth convergence to the best possible solutions.