Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Solving the sorting network problem using iterative optimization with evolved hypermutations
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Multiobjective prototype optimization with evolved improvement steps
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Clustering methods for agent distribution optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Efficient stochastic local search algorithm for solving the shortest common supersequence problem
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Context-sensitive refinements for stochasticoptimization algorithms in inductive logic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Multiobjective evolutionary algorithm for software project portfolio optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Software project portfolio optimization with advanced multiobjective evolutionary algorithms
Applied Soft Computing
Context-sensitive refinements for stochastic optimisation algorithms in inductive logic programming
Artificial Intelligence Review
Evolutionary-based iterative local search algorithm for the shortest common supersequence problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Hyper-Heuristic based on iterated local search driven by evolutionary algorithm
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Evolutionary hyperheuristic for capacitated vehicle routing problem
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Evolutionary algorithms have already been more or less successfully applied to a wide range of optimisation problems. Typically, they are used to evolve a population of complete candidate solutions to a given problem, which can be further refined by some problem-specific heuristic algorithm. In this paper, we introduce a new framework called Iterative Prototype Optimisation with Evolved Improvement Steps. This is a general optimisation framework, where an initial prototype solution is being improved iteration by iteration. In each iteration, a sequence of actions/operations, which improves the current prototype the most, is found by an evolutionary algorithm. The proposed algorithm has been tested on problems from two different optimisation problem domains – binary string optimisation and the traveling salesman problem. Results show that the concept can be used to solve hard problems of big size reliably achieving comparably good or better results than classical evolutionary algorithms and other selected methods.