Integer and combinatorial optimization
Integer and combinatorial optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, GE
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Multiobjective Optimization: Interactive and Evolutionary Approaches
Multiobjective Optimization: Interactive and Evolutionary Approaches
An accelerating technique for population based algorithms
CompSysTech '08 Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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An interactive evolutionary algorithm is presented in the paper, designed to solve integer multi-objective convex integer optimization problems. The algorithm is population-based. A heuristic procedure is used to accelerate the search process, so that the algorithm performs considerably faster than the usual population-based algorithms. The algorithm is developed to perform in the variables' space, but the solutions obtained are evaluated and their values in the objectives' space are used to support the Decision Maker (DM) in the choice of his/her preferences in the form of a reference point in the objectives' space. Comparison is done on an illustrative example between the performance of the new algorithm proposed and SPEA algorithm (Strength Pareto Evolutionary Algorithm). The computational complexity of a single iteration of proposed algorithm is proved to be polynomial.