Adaptive population-based search: Application to estimation of nonlinear regression parameters
Computational Statistics & Data Analysis
Hybrid differential evolution based on fuzzy C-means clustering
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
BBOB-benchmarking the generalized generation gap model with parent centric crossover
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Mutual information neuro-evolutionary system (MINES)
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Precision and dynamics of two mutationally-augmented G3-PCX evolutionary optimization algorithms
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Engineering Applications of Artificial Intelligence
Comparison of cauchy EDA and G3PCX algorithms on the BBOB noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparison of G3PCX and Rosenbrock's algorithms on the BBOB noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
A clustering-based differential evolution for global optimization
Applied Soft Computing
A simulated annealing method based on a specialised evolutionary algorithm
Applied Soft Computing
Solving classification problems using genetic programming algorithms on GPUs
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A global-local optimization approach to parameter estimation of RBF-type models
Information Sciences: an International Journal
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Experimental comparison of six population-based algorithms for continuous black box optimization
Evolutionary Computation
Parameter-less algorithm for evolutionary-based optimization
Computational Optimization and Applications
Hi-index | 0.00 |
In this paper, we propose a population-based, four-step, real-parameter optimization algorithm-generator. The approach divides the task of reaching near the optimum solution into four independent plans of (i) selecting good solutions from a solution base, (ii) generating new solutions using the selected solutions, (iii) choosing inferior or spurious solutions for replacement, and (iv) updating the solution base with good new or old solutions. Interestingly, many classical and evolutionary optimization algorithms are found to be representable by this algorithm-generator. The paper also recommends an efficient optimization algorithm with the possibility of using a number of different recombination plans and parameter values. With a systematic parametric study, the paper finally recommends a real-parameter optimization algorithm which outperforms a number of existing classical and evolutionary algorithms. To extend this study, the proposed algorithm-generator can be utilized to develop new and more efficient population-based optimization algorithms. The treatment of population-based classical and evolutionary optimization algorithms identically through the proposed algorithm-generator is the main hall-mark of this paper and should enable researchers from both classical and evolutionary fields to understand each other’s methods better and interact in a more coherent manner.