An evolutionary approach to combinatorial optimization problems
CSC '94 Proceedings of the 22nd annual ACM computer science conference on Scaling up : meeting the challenge of complexity in real-world computing applications: meeting the challenge of complexity in real-world computing applications
The zero/one multiple knapsack problem and genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
The selfish gene algorithm: a new evolutionary optimization strategy
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Some Guidelines for Genetic Algorithms with Penalty Functions
Proceedings of the 3rd International Conference on Genetic Algorithms
Significance of Locality and Selection Pressure in the Grand Deluge Evolutionary Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Genetic Algorithms for the 0/1 Knapsack Problem
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Ptgas---genetic algorithms evolving noncoding segments by means of promoter/terminator sequences
Evolutionary Computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Towards an analysis of dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An improved genetic algorithm for task allocation in distributed embedded systems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
The role of representations in dynamic knapsack problems
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Constrained optimization problems can be tackled by evolutionary algorithms using penalty functions to guide the search towards feasibility. The core of such approaches is the design of adequate penalty functions. All authors, who designed penalties for knapsack problems, recognized the feasibility problem, i.e. the final population contains unfeasible solutions only. In contrast to previous work, this paper explains the origin of the feasibility problem. Using the concept of fitness segments, a computationally easy analysis of the fitness landscape is suggested. We investigate the effects of the initialization routine, and derive guidelines that ensure resolving the feasibility problem. A new penalty function is proposed that reliably leads to a final population containing feasible solutions, independently of the initialization method employed.