A probabilistic memetic framework

Abstract

Memetic algorithms (MAs) represent one of the

recent growing areas in evolutionary algorithm (EA) research.

The term MAs is now widely used as a synergy of evolutionary or

any population-based approach with separate individual learning

or local improvement procedures for problem search. Quite often,

MAs are also referred to in the literature as Baldwinian EAs,

Lamarckian EAs, cultural algorithms, or genetic local searches.

In the last decade, MAs have been demonstrated to converge

to high-quality solutions more efficiently than their conventional

counterparts on a wide range of real-world problems. Despite the

success and surge in interests on MAs, many of the successful

MAs reported have been crafted to suit problems in very specific

domains. Given the restricted theoretical knowledge available in

the field of MAs and the limited progress made on formal MA

frameworks, we present a novel probabilistic memetic framework

that models MAs as a process involving the decision of embracing

the separate actions of evolution or individual learning and

analyzing the probability of each process in locating the global

optimum. Further, the framework balances evolution and individual

learning by governing the learning intensity of each individual

according to the theoretical upper bound derived while the search

progresses. Theoretical and empirical studies on representative

benchmark problems commonly used in the literature are presented

to demonstrate the characteristics and efficacies of the

probabilistic memetic framework. Further, comparisons to recent

state-of-the-art evolutionary algorithms, memetic algorithms, and

hybrid evolutionary-local search demonstrate that the proposed

framework yields robust and improved search performance.