Journal Information
Swarm and Evolutionary Computation
http://www.journals.elsevier.com/swarm-and-evolutionary-computation/
Impact Factor:
6.330
Publisher:
Elsevier
ISSN:
2210-6502
Viewed:
7246
Tracked:
13

Call For Papers
Introduction:
To tackle complex real world problems, scientists have been looking into natural processes and creatures - both as model and metaphor - for years. Optimization is at the heart of many natural processes including Darwinian evolution, social group behavior and foraging strategies. Over the last few decades, there has been remarkable growth in the field of nature-inspired search and optimization algorithms. Currently these techniques are applied to a variety of problems, ranging from scientific research to industry and commerce. The two main families of algorithms that primarily constitute this field today are the evolutionary computing methods and the swarm intelligence algorithms. Although both families of algorithms are generally dedicated towards solving search and optimization problems, they are certainly not equivalent, and each has its own distinguishing features. Reinforcing each other's performance makes powerful hybrid algorithms capable of solving many intractable search and optimization problems.

About the journal:
Swarm and Evolutionary Computation is the first peer-reviewed publication of its kind that aims at reporting the most recent research and developments in the area of nature-inspired intelligent computation based on the principles of swarm and evolutionary algorithms. It publishes advanced, innovative and interdisciplinary research involving the theoretical, experimental and practical aspects of the two paradigms and their hybridizations. Swarm and Evolutionary Computation is committed to timely publication of very high-quality, peer-reviewed, original articles that advance the state-of-the art of all aspects of evolutionary computation and swarm intelligence. Survey papers reviewing the state-of-the-art of timely topics will also be welcomed as well as novel and interesting applications.

Topics of Interest:
Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.

Applications:
Furthermore, the journal fosters industrial uptake by publishing interesting and novel applications in fields and industries dealing with challenging search and optimization problems from domains such as (but not limited to): Aerospace, Systems and Control, Robotics, Power Systems, Communication Engineering, Operations Research and Decision Sciences, Financial Services and Engineering, (Management) Information Systems, Business Intelligence, internet computing, Sensors, Image Processing, Computational Chemistry, Manufacturing, Structural and Mechanical Designs, Bioinformatics, Computational Biology, Mathematical and Computational Psychology, Cognitive Neuroscience, Brain-computer Interfacing, Future Computing Devices, Nonlinear statistical and Applied Physics, and Environmental Modeling and Software.
Last updated by Dou Sun in 2019-12-08
Special Issues
Special Issue on MEMETIC COMPUTING: ACCELERATING OPTIMIZATION HEURISTICS WITH PROBLEM-DEPENDENT LOCAL SEARCH METHODS
Submission Date: 2020-08-01

For 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. Topics of interest include, but are not limited to: Recent advances on the combination of population-based global optimization solvers with problem-dependent local search procedures. Real-world applications of Memetic Computation and Memetic Algorithms. Evidences of the applicability of Memetic Algorithms and Memetic Computing to emerging paradigms such as Large-Scale Global Optimization, Transfer Optimization or Neuroevolution. Novel insights of Memetic Computing applied to multi- and many-objective optimization. Memetic Algorithms for symbolic regression and time-series prediction. Complete Anytime Memetic Algorithms (MAs that can deliver feasible solutions if stopped but that will stop by themselves if they have found the optimal solution). New findings on memetic transmission, design selection and design patterns. Advances on co-evolving methods and self-adaptive memetic schemes. Theoretical and practical studies exploring the balance between exploration and exploitation in MAs. New procedures for detecting and quantifying the level of stagnation on MAs, and novel trends for enhancing diversification.
Last updated by Dou Sun in 2020-04-28
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