Membrane Computing: An Introduction
Membrane Computing: An Introduction
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
A membrane algorithm for the min storage problem
WMC'06 Proceedings of the 7th international conference on Membrane Computing
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A Quantum-Inspired Evolutionary Algorithm Based on P systems for Knapsack Problem
Fundamenta Informaticae
Distributed differential evolution with explorative---exploitative population families
Genetic Programming and Evolvable Machines
Membrane computing as a modeling framework: cellular systems case studies
SFM'08 Proceedings of the Formal methods for the design of computer, communication, and software systems 8th international conference on Formal methods for computational systems biology
An improved membrane algorithm for solving time-frequency atom decomposition
WMC'09 Proceedings of the 10th international conference on Membrane Computing
Analyzing radar emitter signals with membrane algorithms
Mathematical and Computer Modelling: An International Journal
A Quantum-Inspired Evolutionary Algorithm Based on P systems for Knapsack Problem
Fundamenta Informaticae
A membrane algorithm with quantum-inspired subalgorithms and its application to image processing
Natural Computing: an international journal
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In this paper we present an analysis of the similarities between distributed evolutionary algorithms and membrane systems. The correspondences between evolutionary operators and evolution rules and between communication topologies and policies in distributed evolutionary algorithms and membrane structures and communication rules in membrane systems are identified. As a result of this analysis we propose new strategies of applying the operators in evolutionary algorithms and new variants of distributed evolutionary algorithms. The behavior of these variants is numerically tested for some continuous optimization problems.