jMeme: A Java library for designing Competent Memetic Algorithms
Giovanni Acampora, Autilia Vitiello · 2016
Memetic Algorithms (MAs) are a family of metaheuristics which combines global search approaches with local search techniques. Thanks to this hybridization, MAs have emerged as a powerful tool for tackling hard optimization problems in a more efficient way than their `no hybrid' counterparts. However, despite the success of MAs, it is still vital to note that the design of MAs raises a number of important issues which must be addressed to achieve a suitable hybridization of global and local search approaches resulting in a so-called Competent Memetic Algorithm (CMA). Currently, no software tools exist to automatically design CMAs and, as a consequence, all the hybridization choices are responsibility of human designers, which suffer from slowness and propensity to make mistakes. In order to bridge this design gap, this paper introduces jMeme, a Java library enabling an automatic development of CMAs so as to free practitioners of the responsibility for the design choices. As shown by experiments involving well-known benchmark functions, the competent design of MAs performed by jMeme yields better performance than MAs designed conventionally.