Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Ant Colony Optimization
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
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In this paper, we propose a real-valued evolutionary algorithm being guided by directional information. We derive direction of improvement from a set of elite solutions, which is always maintained overtime. A population of solutions is evolved over time under the guidance of those directions. At each iteration, there are two types of directions that are being generated: (1) convergence direction between an elite solution (stored in an external set) and a second-ranked solution from the current population, and (2) spreading direction between two elite solutions in the external set. These directions are then used to perturb the current population to get an offspring population. The combination of the offsprings and the elite solutions is used to generate a new set of elite solutions as well as a new population. A case study has been carried out on a set of difficult problems investigating the performance and behaviour of our newly proposed algorithm. We also validated its performance with 12 other well-known algorithms in the field. The proposed algorithm showed a good performance in comparison with these algorithms.