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.

Evolutionary-based Image Segmentation Methods

“…Example 2. Let the two parent individuals are (6,19,91,38,64) and (3,29,17,61,6), randomly generate the mask (1, 0, 0, 1, 0), then the two offspring after crossover are (6, 29, …”

“…Recently, MAs have been well used across a wide range of problem domains. A lot of studies have demonstrated that MAs converge to high-quality solutions more efficiently than their conventional counterparts in many realworld applications [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. MAs have also been applied in the image processing field [33][34][35][36][37][38].…”

Hybrid Iterative Algorithm of Asymptotically Nonexpansive Mappings for Equilibrium Problems

“…However, the analytical optimal solution is difficult to obtain even for relatively simple application problems. Researchers instead study the numerical optimization algorithm arises from almost every field, such as engineering design, systems operation, decision making, and computer science for example [2][3][4].…”

Meta-Lamarckian learning in three stage optimal memetic exploration

“…In [4] the classification of coordination methods has been extended and updated. The following four categories have been identified: 1) Adaptive Hyper-heuristic, where heuristic rules are employed (e.g., [8], [9], [10], [11]); 2) MetaLamarckian learning defined in [12], where the activation of the memes depends on their success, see also [13], [14], [15]; 3) Self-Adaptive and Co-Evolutionary, where the rules coordinating the memes are evolving in parallel with the candidate solutions of the optimization problem or encoded within the solution, see [16], [17], [18], [19]; and 4) Fitness DiversityAdaptive, where the activation of the memes depends on a measure of the diversity (e.g., [20], [21], [6], [22], [23]). As a general idea, the algorithmic designer attempts to have a system which performs the coordination automatically.…”