Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Essays and Surveys in Metaheuristics
Essays and Surveys in Metaheuristics
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming Theory and Practice V (Genetic and Evolutionary Computation)
Genetic Programming Theory and Practice V (Genetic and Evolutionary Computation)
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
Prime number generation using memetic programming
Artificial Life and Robotics
| Hi-index | 0.00 |
Meta-heuristics are general frameworks of heuristics methods for solving combinatorial optimization problems, where exploring the exact solutions for these problems becomes very hard due to some limitations like extremely large running time. In this paper, new local searches over tree space are defined. Using these local searches, various meta-heuristics can be generalized to deal with tree data structures to introduce a more general framework of meta-heuristics called Meta-Heuristics Programming (MHP) as general machine learning tools. As an alternative to Genetic Programming (GP) algorithm, Memetic Programming (MP) algorithm is proposed as a new outcome of the MHP framework. The efficiency of the proposed MP Algorithm is examined through comparative numerical experiments.