Artificial Life Algorithm for Function Optimization
Bo‐Suk Yang, Yun-Hi Lee · 2000
Abstract This paper presents an enhanced artificial life algorithm for function optimization. As artificial life organisms have a sensing system, they can find the resource they want and metabolize it. The characteristics of artificial life are emergence and dynamic interaction with the environment. In other words, the micro-interaction with each other in the artificial life’s group results in emergent colonization in the whole system. The optimizing ability and convergent characteristics of this proposed algorithm is verified by using three well-known test functions. The numerical results also show that the proposed algorithm is superior to the genetic algorithm and immune genetic algorithm for the multimodal function.