Journal ArticleDOI
Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms
William B. Langdon,Riccardo Poli +1 more
TLDR
In this article, the authors use evolutionary computation to automatically find problems which demonstrate the strength and weaknesses of modern search heuristics, such as particle swarm optimization, differential evolution, and covariance matrix adaptation-evolution strategy (CMA-ES).Abstract:
We use evolutionary computation (EC) to automatically find problems which demonstrate the strength and weaknesses of modern search heuristics. In particular, we analyze particle swarm optimization (PSO), differential evolution (DE), and covariance matrix adaptation-evolution strategy (CMA-ES). Each evolutionary algorithm is contrasted with the others and with a robust nonstochastic gradient follower (i.e., a hill climber) based on Newton-Raphson. The evolved benchmark problems yield insights into the operation of PSOs, illustrate benefits and drawbacks of different population sizes, velocity limits, and constriction (friction) coefficients. The fitness landscapes made by genetic programming reveal new swarm phenomena, such as deception, thereby explaining how they work and allowing us to devise better extended particle swarm systems. The method could be applied to any type of optimizer.read more
Citations
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Journal ArticleDOI
Differential Evolution: A Survey of the State-of-the-Art
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
Journal ArticleDOI
Recent advances in differential evolution – An updated survey
TL;DR: It is found that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research on DE.
Journal ArticleDOI
An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization
TL;DR: A new mutation strategy, a fitness- induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance are proposed.
Journal ArticleDOI
DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization
TL;DR: Compared with other state-of-the-art DE approaches, DE/BBO performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate.
Journal ArticleDOI
Differential Evolution: A survey of theoretical analyses
Karol Opara,Jarosław Arabas +1 more
TL;DR: The theoretical results obtained so far for Differential Evolution are surveyed and a comprehensive view on the current-day understanding of the underlying mechanisms of DE is complemented by a list of promising research directions.
References
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Journal ArticleDOI
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Book
Genetic Programming: On the Programming of Computers by Means of Natural Selection
TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.
Journal ArticleDOI
No free lunch theorems for optimization
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
Journal ArticleDOI
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
M. Clerc,James Kennedy +1 more
TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.
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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
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