Special Issue Memetic Computing: Accelerating Optimization Heuristics with Problem-Dependent Local Search Methods

Members of the JRL  Eneko Osaba and Javier Del Ser, along with Dr. Carlos Cotta and Prof. Dr. Pablo Moscato, will act as guest editors of the special issue Memetic Computing: Accelerating Optimization Heuristics with Problem-Dependent Local Search Methods in Swarm and Evolutionary Computation Journal (Q1, I.F.: 6,330). The special issue is now open for receiving submissions, and this is the CFP:

Resultado de imagen de swarm and evolutionary computationFor at least the last three decades of the field of Evolutionary Computing, a growing number of researchers have focused their efforts on combining different methods and functionalities into a single solver. In general, the aim was to overcome disadvantages of some individual solvers and/or to improve the performance rendered by off-the-shelf optimization methods. In this regard, Memetic Algorithms (MA) spearhead this design principle by exploiting the synergies of individual search procedures in evolutionary optimization frameworks leading to development of the Memetic Computing (MC) field. Since its inception by Moscato and Norman in late ’80s, MC has blossomed into a manifold of algorithmic variants, to yield one of the most prolific areas within Swarm Intelligence and Evolutionary Computation to date. Indeed, MC have been growing fast to yield complex techniques with extremely sophisticated exploitation and cooperation mechanisms. A variety of MAs continue to use Evolutionary/Bio-inspired/Swarm Intelligence approaches for global optimization (both combinatorial and non-linear or mixed) with separate individual improvement and adaptive or learning mechanisms, generally incorporating domain-specific knowledge for the problem under analysis.

This special issue aims at disseminating the latest findings and research achievements in MAs, with a special attention paid to contributions focused on problem-dependent individual/local search methods and solutions. We also welcome theoretical research ideas and their application to real-world problems. To this end, we solicit high-quality original submissions to this special issue that reflect the unprecedented momentum garnered by this research area.

For more information: https://www.journals.elsevier.com/swarm-and-evolutionary-computation/call-for-papers/special-issue-on-memetic-computing-accelerating-optimization

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