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Memetic Computing

Memetic Computing OnlineFirst articles

26.10.2020 | Regular Research Paper

Evolution of biocoenosis through symbiosis with fitness approximation for many-tasking optimization

Memetic computing is a blooming research area, which treats memes as the fundamental building blocks of information transfer. Evolutionary multitasking is an emerging topic in memetic computation, which applies evolutionary algorithm to optimize …

22.10.2020 | Regular Research Paper

Feature selection based bee swarm meta-heuristic approach for combinatorial optimisation problems: a case-study on MaxSAT

Meta-heuristics are high-level concepts and strategies for finding near-optimal solutions within a reasonable response time. They are widely used in different fields of applications [ 1 ] to solve both classical problems such as vehicle routing [ 2 …] and more recent ones such as crowdsensing [

19.10.2020 | Regular Research Paper

Multi-task gradient descent for multi-task learning

Multi-Task Learning (MTL) aims to simultaneously solve a group of related learning tasks by leveraging the salutary knowledge memes contained in the multiple tasks to improve the generalization performance. Many prevalent approaches focus on …

13.10.2020 | Regular Research Paper

An efficient memetic genetic programming framework for symbolic regression

Genetic programming (GP) is a popular evolutionary algorithm which has been proved quite effective in automatic computer program generation [ 13 , 20 ]. In GP, computer programs are represented as trees and evolved using genetic operators such as …

12.10.2020 | Regular Research Paper

A unified linear convergence analysis of k-SVD

Eigenvector computation, e.g., k-SVD for finding top-k singular subspaces, is often of central importance to many scientific and engineering tasks. There has been resurgent interest recently in analyzing relevant methods in terms of singular value …

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Über diese Zeitschrift

Memetic Computing's goals are:

  • To be an outlet for high quality research in hybrid metaheuristics (including evolutionary hybrids) for optimization, control and design in continuous and discrete optimization domains. We seek to dissolve the barriers separating metaheuristics, exact and approximation algorithms research and to bring forth a renewed impetus towards the investigation and understanding of promising new hybrid algorithmic technologies.
  • To go beyond current search methodologies towards innovative research on the emergence of cultural artifacts such as game, trade and negotiation strategies and, more generally, rules of behavior as they apply to, for example, robotic, multi-agent and artificial life systems.
  • Ultimately, Memetic Computing aspires to serve as a focal publication where the latest results in Natural Computation, Artificial Intelligence, Machine Learning, Operational Research and Natural Sciences (e.g. cognitive, animal and insect's behavior, etc.) are fuzzed together in novel ways in order to transcend the intrinsic limitations of a single discipline.
  • Potential authors are invited to submit original research articles for publication consideration. Reviews and short communications may also be considered.

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