A genetic algorithm for the job shop problem
Computers and Operations Research - Special issue on genetic algorithms
A genetic approach to the quadratic assignment problem
Computers and Operations Research - Special issue on genetic algorithms
Evolutionary algorithms for constrained engineering problems
Computers and Industrial Engineering
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
CIE '96 Proceedings of the 19th international conference on Computers and industrial engineering
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
A greedy genetic algorithm for the quadratic assignment problem
Computers and Operations Research
An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
Flight graph based genetic algorithm for crew scheduling in airlines
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
The hybrid heuristic genetic algorithm for job shop scheduling
Computers and Industrial Engineering
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Hybrid genetic algorithms for constrained placement problems
IEEE Transactions on Evolutionary Computation
A multistage evolutionary algorithm for the timetable problem
IEEE Transactions on Evolutionary Computation
Solving the quadratic assignment problem with clues from nature
IEEE Transactions on Neural Networks
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Genetic algorithm is a novel optimization technique for solving constrained optimization problems. The penalty function methods are the popular approaches because of their simplicity and ease of implementation. Penalty encoding method needs more generations to get good solutions because it causes invalid chromosomes during evolution. In order to advance the performance of Genetic Algorithms for solving production allocation problems, this paper proposes a new encoding method, which applies the upper/lower bound concept of dynamic programming decision path on the chromosome encoding of genetic algorithm, that encodes constraints into chromosome to ensure that chromosomes are all valid during the process of evolution. Utilization of the implicated parallel processing characteristic of genetic algorithms to improve dynamic programming cannot guarantee to solve complex problems in the polynomial time. Additionally, a new simultaneous crossover and mutation operation is proposed to enable the new method to run correctly following the standard genetic algorithm procedures. This approach is evaluated on some test problems. Solutions obtained by this approach indicate that our new encoding genetic algorithms certainly accelerate the performance of the evolution process.