Multi-Parent Crossover Algorithm with Discrete Recombination

As a novel and promising algorithm, differential evolution (DE) has shown good performance in lots of optimization problems. It has been said that DE is one of the most competitive EAs for continuous optimization. As a kind of EAs, GT algorithm is a novel algorithm which based on multi-parent crossover. Compared with GT algorithm, DE performances better to find the global minima obviously. This paper presents a concept of pattern analyses to analyze the reason of the DE´s high performance. Then a new algorithm based on GT´s multi-parent crossover and traditional DE´s discrete recombination is presented for enhancing the performance of the obsolete and inefficient GT algorithm. According to the pattern analyses, the new algorithm obtains several patterns similar to DE. The experiments show the efficiency of the proposed new algorithm.