Individual evolutionary algorithm and its application to learning of nearest neighbor based MLP

A society S(I,T) is defined as a system consisting of an individual set I and a task set T. This paper studies the problem to find an efficient S such that all tasks in T can be fulfilled using the smallest I. The individual evolutionary algorithm (IEA) is proposed to solve this problem. By IEA, each individual finds and adapts itself to a class of tasks through evolution, and an efficient S can be obtained automatically. The EIA consists of four operations: competition, gain, less and retraining. Competition tests the performance of the recent I and the fitness of each individual; gain increases the performance of I by adding new individuals; loss makes I more compact by removing individuals with very low fitness, and individuals are adjusted by retraining to make them better. An evolution cycle is: competition boolean AND (gain boolean OR loss) boolean AND retraining, and the evolution is performed cycle after cycle until some criterion is satisfied. The performance of IEA is verified by applying it to the learning of nearest neighbor based multilayer perceptrons.