Classification of adaptive memetic algorithms: a comparative study

By Yew-Soon Ong, Meng-Hiot Lim, Ning Zhu, K. Wong. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2006

546 citations recorded by the source corpus.

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/c800009d3933cc555ce025feb79fdb1c069d5c19

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework (2007, methodology): “In line with the observations in [ 12 , 14], conducting local search on all chromosomes can result in unnecessary computation.”
Bio-inspired computation: Where we stand and what's next (2019, background): “[268], in which an extensive survey on Memetic Algorithms considering multiple learning methods inside an evolutionary strategy is presented.”
Accelerating Differential Evolution Using an Adaptive Local Search (2008, background): “An excellent taxonomy and comparative study on adaptive choice of memes in MAs is presented in [20].”
Memetic algorithms and memetic computing optimization: A literature review (2012, methodology): “selection strategies, both based on the roulette wheel selection, have been tested for the meme selection, see [164, 166].”
Memetic Computation—Past, Present & Future [Research Frontier] (2010, background): “The incorporation or embedding of knowledge about the underlying problem within the search algorithms is now generally accepted as being beneficial for improving search performance [35] [36][37] .”
A Multi-Facet Survey on Memetic Computation (2011, background): “and parameters for problem-solving [17], [128].”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “…impossible or extremely difficult by using the classical optimization techniques (Karaboga and Akay 2009a; Nowak and Cirpka 2004; Clerc and Kennedy 2002; Trelea 2003; Dorigo et al. 1996; Ong et al. 2006; Storn and Price 1997; Yang and Deb 2010; Das et al. 2011; Yang 2005, 2009; Zhang et al. 2007).”
An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling (2007, methodology): “In [ 29 ], a classification of memes adaptation in adaptive MAs was presented on the basis of the mechanism used and the level of historical knowledge on the memes employed, and the global convergence properties of adaptive MAs were analyzed by means of finite Markov chain.”
Markov blanket-embedded genetic algorithm for gene selection (2007, background): “One way to solve this problem is to hybridize GA with some memetic operations (also known as local search operations) [30] [31] [32] [33] [34] which are capable of fine-tuning and improving the solutions generated by the GA to make them more accurate and efficient.”
Generalizing Surrogate-Assisted Evolutionary Computation (2010, background): “First, we aim to exploit MAs’ capability of locating the local and global optima efficiently.”