For empirical research on computer algorithms, it is essential to have a set of benchmark problems on which the relative performance of different methods and their applicability can be assessed. In the majority of computational research fields there are established sets of benchmark problems; however, the field of genetic programming lacks a similarly rigorously defined set of benchmarks. There is a strong interest within the genetic programming community to develop a suite of benchmarks. Following recent surveys [7], the desirable characteristics of a benchmark problem are now better defined. In this paper the Tartarus problem is proposed as a tunably difficult benchmark problem for use in Genetic Programming. The justification for this pr...
The current state-of-the-art of genetic algorithms is dominated by high-performing specialistsolvers...
Genetic algorithms are a powerful tool for solving search and optimization problems. We examine the ...
This paper addresses the issue of what makes a problem genetic programming (GP)-hard by considering ...
For empirical research on computer algorithms, it is essential to have a set of benchmark problems o...
Not so many benchmark problems have been proposed in the area of Genetic Programming (GP). In this s...
There have been several papers published relating to the practice of benchmarking in machine learnin...
11siGenetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its ben...
article/10.1007%2Fs10710-012-9177-2 Abstract We present the results of a community survey regarding ...
Introduction Given the multiplicity of GP programs that could produce the correct solution for a pa...
IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, May 25-28, 2015, Proceedings, Se...
9siWe present the results of a community survey regarding genetic programming benchmark practices. A...
Genetic Algorithms are bio-inspired metaheuristics that solve optimization problems; they are evolut...
Genetic programming (GP) is an automated method for creating a working computer program from a high-...
This thesis addresses the issues associated with conventional genetic algorithms (GA) when applied t...
Genetic Programming is an evolutionary computation technique which searches for those computer progr...
The current state-of-the-art of genetic algorithms is dominated by high-performing specialistsolvers...
Genetic algorithms are a powerful tool for solving search and optimization problems. We examine the ...
This paper addresses the issue of what makes a problem genetic programming (GP)-hard by considering ...
For empirical research on computer algorithms, it is essential to have a set of benchmark problems o...
Not so many benchmark problems have been proposed in the area of Genetic Programming (GP). In this s...
There have been several papers published relating to the practice of benchmarking in machine learnin...
11siGenetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its ben...
article/10.1007%2Fs10710-012-9177-2 Abstract We present the results of a community survey regarding ...
Introduction Given the multiplicity of GP programs that could produce the correct solution for a pa...
IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, May 25-28, 2015, Proceedings, Se...
9siWe present the results of a community survey regarding genetic programming benchmark practices. A...
Genetic Algorithms are bio-inspired metaheuristics that solve optimization problems; they are evolut...
Genetic programming (GP) is an automated method for creating a working computer program from a high-...
This thesis addresses the issues associated with conventional genetic algorithms (GA) when applied t...
Genetic Programming is an evolutionary computation technique which searches for those computer progr...
The current state-of-the-art of genetic algorithms is dominated by high-performing specialistsolvers...
Genetic algorithms are a powerful tool for solving search and optimization problems. We examine the ...
This paper addresses the issue of what makes a problem genetic programming (GP)-hard by considering ...