jMeme is the first software library enabling an automatic design of Competent Memetic Algorithms (CMAs) so as to identify the best hybridization configuration and free practitioners of the responsibility for the design choices. In fact, jMeme is a Java-based framework that addresses the design issues of the Memetic Algorithms (MAs) by providing a set of mechanisms to support integration between local and global searches in a simple and direct way. Natively, jMeme includes several global search methods among the most popular ones such as Genetic Algorithms (GAs), Differential Evolution (DE), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Big Bang-Big Crunch (BBBC) as well as some local search techniques such as Hill Climbing (HC), Simulated Annealing (SA), Tabu Search (TS), Hooke Jeeves (HJ) and Chaotic Local Search (CLS). However, jMeme provides a collection of abstract classes and interfaces that model the general concepts of global method and local procedures in order to support the extendibility.
G. Acampora and A. Vitiello, "jMeme: a Java Library for Designing Competent Memetic Algorithms," 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, Canada, 24-29 July 2016.