Using Traceless Genetic Programming for Solving Multiobjective Optimization Problems
Traceless Genetic Programming (TGP) is a Genetic Programming (GP) variant that is used in cases where the focus is rather the output of the program than the program itself. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. Two genetic operators are used in conjunction with TGP: crossover and insertion. In this paper, we shall focus on how to apply TGP for solving multi-objective optimization problems which are quite unusual for GP. Each TGP individual stores the output of a computer program (tree) representing a point in the search space. Numerical experiments show that TGP is able to solve very fast and very well the considered test problems.
Code (1)
Tasks
Multiobjective OptimizationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Solving classification problems using Traceless Genetic Programming
Traceless Genetic Programming (TGP) is a new Genetic Programming (GP) that may be used for solving difficult real-world problems. The main difference between TGP and other GP techniques is that TGP does not explicitly st…
ClassificationSolving even-parity problems using traceless genetic programming
A genetic programming (GP) variant called traceless genetic programming (TGP) is proposed in this paper. TGP is a hybrid method combining a technique for building individuals and a technique for representing individuals.…
Transfer Learning based Dynamic Multiobjective Optimization Algorithms
One of the major distinguishing features of the dynamic multiobjective optimization problems (DMOPs) is the optimization objectives will change over time, thus tracking the varying Pareto-optimal front becomes a challeng…
BIG-bench Machine LearningMultiobjective OptimizationTransfer LearningMultiobjective optimization in Gene Expression Programming for Dew Point
The processes occurring in climatic change evolution and their variations play a major role in environmental engineering. Different techniques are used to model the relationship between temperatures, dew point and relati…
Multiobjective OptimizationEnsemble of heterogeneous flexible neural trees using multiobjective genetic programming
Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic progr…
ClassificationDiversityTime SeriesTime Series Analysis+1