Journal ArticleDOI
A comparative study of Artificial Bee Colony algorithm
Dervis Karaboga,Bahriye Akay +1 more
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Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.About:
This article is published in Applied Mathematics and Computation.The article was published on 2009-08-01. It has received 2835 citations till now. The article focuses on the topics: Artificial bee colony algorithm & Meta-optimization.read more
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Evolutionary Cost-Sensitive Extreme Learning Machine
Lei Zhang,David Zhang +1 more
TL;DR: This paper proposes an evolutionary cost-sensitive ELM, which to the best of the authors' knowledge, is the first proposal of ELM in evolutionary Cost-sensitive classification scenario, and well addresses the open issue of how to define the cost matrix in cost- sensitive learning tasks.
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Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems
TL;DR: A novel metaheuristic algorithm called queuing search (QS), which is inspired from human activities in queuing, is presented, which is tested on 30 bound-constrained benchmark functions scalable with 30 and 100 dimensions from CEC 2014, 5 standard and 4 challenging constrained engineering optimization problems.
Journal ArticleDOI
On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget.
TL;DR: Results of more than 800,000 runs on 800 randomly generated tests show that both stochastic nature-inspired metaheuristics and deterministic global optimization methods are competitive and surpass one another in dependence on the available budget of function evaluations.
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Application of evolutionary computation for rule discovery in stock algorithmic trading
TL;DR: The review reveals the research focus and gaps in applying EC techniques for rule discovery in stock AT and suggests a roadmap for future research.
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Multi-strategy ensemble grey wolf optimizer and its application to feature selection
TL;DR: The experimental results show that the proposed MEGWO, which integrate multiple improved search strategies, outperforms other variants of GWO and other algorithms in terms of accuracy and convergence speed.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
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.
Book
Self-Organizing Maps
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.