Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Genetic diversity as an objective in multi-objective evolutionary algorithms
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A genetic algorithm and a particle swarm optimizer hybridized with Nelder-Mead simplex search
Computers and Industrial Engineering - Special issue: Sustainability and globalization: Selected papers from the 32 nd ICC&IE
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Empirical investigation of multiparent recombination operators in evolution strategies
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A hybrid evolutionary multi-objective and SQP based procedure for constrained optimization
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
A Hybrid Simplex Multi-objective Evolutionary Algorithm Based on Preference Order Ranking
CIS '11 Proceedings of the 2011 Seventh International Conference on Computational Intelligence and Security
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
The key issue for an efficient and reliable multi-objective evolutionary algorithm (MOEA) is the ability to converge to the True Pareto Front with the least number of objective function evaluations, while covering it as much as possible. To this purpose, in a previous paper performance comparisons showed that the Genetic Diversity Evolutionary Algorithm (GeDEA) was at the same level of the best state-of-the-art MOEAs due to it intrinsic ability to properly conjugate exploitation of current non-dominated solutions and the exploration of the search space. In this paper, an improved version, namely the GeDEA-II, is proposed which features a novel crossover operator, the Simplex-Crossover (SPX), and a novel mutation operator, the Shrink-Mutation. Genetic Diversity Evaluation Method (GeDEM) operator was left unchanged and completed using the non-dominated-sorting based on crowding distance. The performance of the GeDEA-II was tested against other different state-of-the-art MOEAs, following a well-established procedure already used in other previous works. When compared to the original proposed test problems, the number of decision variables was increased and the number of generations left to the algorithms was intentionally reduced in order to test the convergence performance of the MOEAs. GeDEA-II and competitors were executed 30 times on each proposed test problem. The final approximation set reached by each algorithm was represented in the objective function space, and the performance, measured in terms of hypervolume indicator, reported in dedicated box plots. Finally, authors aimed at putting in evidence the excellent performance of GeDEA-II on the same test problems, by increasing the decision variables up to 100 times the original proposed number. Results clearly indicates that the performance of GeDEA-II is, at least in these cases, superior.