A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Cooperative versus competitive coevolution for Pareto multiobjective optimization
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Solving channel borrowing problem with coevolutionary genetic algorithms
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Handling uncertainties in evolutionary multi-objective optimization
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
ARO: A new model-free optimization algorithm inspired from asexual reproduction
Applied Soft Computing
Genetic algorithms for multi-objectives problems under its objective boundary
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
Expert Systems with Applications: An International Journal
Preference-driven co-evolutionary algorithms show promise for many-objective optimisation
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Local preference-inspired co-evolutionary algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Acceleration of evolutionary image filter design using coevolution in cartesian GP
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Computers and Operations Research
On finding well-spread pareto optimal solutions by preference-inspired co-evolutionary algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA yields poor coverage across the Pareto front, yet finds a solution that dominates all the solutions produced by the eight other algorithms.