Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Innovization: innovating design principles through optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A robust evolutionary framework for multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multi-resistant radar jamming using genetic algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Many Objective Optimisation: Direct Objective Boundary Identification
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Radar waveform optimisation as a many-objective application benchmark
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Dominance-Based Multiobjective Simulated Annealing
IEEE Transactions on Evolutionary Computation
Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch
IEEE Transactions on Evolutionary Computation
Multiobjective GAs, quantitative indices, and pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Radar system design and optimization are complex problems recently cast in the framework of multi-objective evolutionary algorithms. However, in the problem of counter radar detection and tracking, the state-of-the-art multi-objective optimization algorithm NSGA-II is unable to span the complete 2D Pareto front of the asymptotic and convex problem domain, leaving out vital information on the radar-jammer system dynamics. Common modifications to the domination principle employed will to some degree increase the span of the Pareto front, at the expense of slower convergence and a less dense front. In this paper, the new Surface Evolutionary Algorithm (SEA) is introduced to overcome these problems. The SEA characterizes all solutions by one single metric and uses interpolated attraction points along the boundary of the solution set as basis for selecting and evolving solutions in the optimizer. The SEA is proposed and analyzed in the context of the conflicting multi-objective optimization criteria of search efficiency, density distribution and span of the complete Pareto front of the counter radar detection problem. The SEA is shown to produce high performance solutions not easily obtained using the well-established optimization methods of NSGA-II and e-MOEA.