Memetic Computation—Past, Present &amp; Future [Research Frontier

Ong, Yew-Soon; Lim, Meng‐Hiot; Chen, Xianshun

doi:10.1109/mci.2010.936309

Cited by 395 publications

(111 citation statements)

References 44 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…. a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem solving" (see Ong et al, 2010;Chen et al, 2011;Chen and Ong, 2012). These models can be accomplished at a variety of levels (Ong et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

A study on meme propagation in multimemetic algorithms

Nogueras

Cotta

2015

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

Multimemetic algorithms (MMAs) are a subclass of memetic algorithms in which memes are explicitly attached to genotypes and evolve alongside them. We analyze the propagation of memes in MMAs with a spatial structure. For this purpose we propose an idealized selecto-Lamarckian model that only features selection and local improvement, and study under which conditions good, high-potential memes can proliferate. We compare population models with panmictic and toroidal grid topologies. We show that the increased takeover time induced by the latter is essential for improving the chances for good memes to express themselves in the population by improving their hosts, hence enhancing their survival rates. Experiments realized with an actual MMA on three different complex pseudo-Boolean functions are consistent with these findings, indicating that memes are more successful in a spatially structured MMA, rather than in a panmictic MMA, and that the performance of the former is significantly better than that of its panmictic counterpart.

show abstract

Section: Introductionmentioning

confidence: 99%

A study on meme propagation in multimemetic algorithms

Nogueras

Cotta

2015

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

show abstract

“…As consequence, a gradient descent method is here embedded to improve accuracy and speed up the learning process. Therefore, this hybridisation of global/local learning, denoted as memetic learning [14,15], allows for finding a suitable predictor which accurately models the data in a simple-to-complex fashion independently from any kind of initial rule number assumption.…”

Section: Introductionmentioning

confidence: 99%

Memetic Type-2 Fuzzy System Learning for Load Forecasting

León¹,

Taylor²

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

View full text Add to dashboard Cite

This paper presents an automatic method to design interval type-2 fuzzy systems for load forecasting applications using a memetic algorithm. This hybridisation of a variable-length genetic algorithm and a gradient descent method allows for concurrent learning of the system's parameters and structure in a versatile fashion. Results are presented addressing chaotic system and market-level one-day-ahead load forecasting.

show abstract

“…While this interpretation remains compatible with classical MA approaches, it also opens up the door to more complex strategies and suggests an underlying connection of MAs with multi-agent systems. Indeed, the current notion of memetic computing as a paradigm that uses memes as units of information encoded in computational representations for the purpose of problem-solving can easily lead to a co-evolving system of intelligent agents [14], see also [15][16][17]. In this context, a meme is regarded as a lifetime learning procedure capable of locally improving solutions [18][19][20], although a more general interpretation is also possible in terms of culturally learned traits [21].…”

Section: Introductionmentioning

confidence: 99%

Asymptotic guarantee of success for multi-agent memetic systems

Byrski¹,

Schaefer²,

Smółka³

et al. 2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

View full text Add to dashboard Cite

Abstract. The paper introduces a stochastic model for a class of population-based global optimization meta-heuristics, that generalizes existing models in the following ways. First of all, an individual becomes an active software agent characterized by the constant genotype and the meme that may change during the optimization process. Second, the model embraces the asynchronous processing of agent's actions. Third, we consider a vast variety of possible actions that include the conventional mixing operations (e.g. mutation, cloning, crossover) as well as migrations among demes and local optimization methods. Despite the fact that the model fits many popular algorithms and strategies (e.g. genetic algorithms with tournament selection) it is mainly devoted to study memetic algorithms.The model is composed of two parts: EMAS architecture (data structures and management strategies) allowing to define the space of states and the framework for stochastic agent actions and the stationary Markov chain described in terms of this architecture. The probability transition function has been obtained and the Markov kernels for sample actions have been computed.The obtained theoretical results are helpful for studying metaheuristics conforming to the EMAS architecture. The designed synchronization allows the safe, coarse-grained parallel implementation and its effective, sub-optimal scheduling in a distributed computer environment. The proved strong ergodicity of the finite state Markov chain results in the asymptotic stochastic guarantee of success, which in turn imposes the liveness of a studied metaheuristic. The Markov chain delivers the sampling measure at an arbitrary step of computations, which allows further asymptotic studies, e.g. on various kinds of the stochastic convergence.

show abstract

Memetic Computation—Past, Present & Future [Research Frontier

Cited by 395 publications

References 44 publications

A study on meme propagation in multimemetic algorithms

A study on meme propagation in multimemetic algorithms

Memetic Type-2 Fuzzy System Learning for Load Forecasting

Asymptotic guarantee of success for multi-agent memetic systems

Contact Info

Product

Resources

About