Proceedings of: 15th International Conference on Machine Learning, Madison (Wisconsin, USA), July 24-27, 1998.Genetic Programming (GP) is a machine learning technique that was not conceived to use domain knowledge for generating new candidate solutions. It has been shown that GP can benefit from domain knowledge obtained by other machine learning methods with more powerful heuristics. However, it is not obvious that a combination of GP and a knowledge intensive machine learning method can work better than the knowledge intensive method alone. In this paper we present a multi-strategy approach where an analytical and inductive approach (hamlet) and an evolutionary technique based on GP (EvoCK) are combined for the task of learning control ru...
There are several ways of applying Genetic Programming (GP) to STRIPS-like planning in the literat...
This paper presents a study of different methods of using incremental evolution with genetic program...
Recent experiments with a genetic-based encoding schema are presented as a potentially useful tool i...
Proceedings of: 15th International Conference on Machine Learning, Madison (Wisconsin, USA), July 24...
AbstractThe purpose of this article is to present a multi-strategy approach to learn heuristics for ...
The purpose of this article is to present a multi-strategy approach to learn heuristics for planning...
Proceeding of: 7th International Conference on Evolutionary Programming, EP98 San Diego, California,...
This paper presents an evolutionary approach and an incremental approach to find learning rules of s...
Genetic programming (GP) is an automated method for creating a working computer program from a high-...
There have been many applications of artificial intelligence data mining recently. One of its many b...
Introduction Genetic programming is a domain-independent problem-solving approach in which computer ...
There are many different approaches to solving planning problems, one of which is the use of domain ...
Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming ...
Declarative problem solving, such as planning, poses interestig challenges for Genetic Programming ...
This work investigates the application of Evolutionary Computation (EC) to the induction of generali...
There are several ways of applying Genetic Programming (GP) to STRIPS-like planning in the literat...
This paper presents a study of different methods of using incremental evolution with genetic program...
Recent experiments with a genetic-based encoding schema are presented as a potentially useful tool i...
Proceedings of: 15th International Conference on Machine Learning, Madison (Wisconsin, USA), July 24...
AbstractThe purpose of this article is to present a multi-strategy approach to learn heuristics for ...
The purpose of this article is to present a multi-strategy approach to learn heuristics for planning...
Proceeding of: 7th International Conference on Evolutionary Programming, EP98 San Diego, California,...
This paper presents an evolutionary approach and an incremental approach to find learning rules of s...
Genetic programming (GP) is an automated method for creating a working computer program from a high-...
There have been many applications of artificial intelligence data mining recently. One of its many b...
Introduction Genetic programming is a domain-independent problem-solving approach in which computer ...
There are many different approaches to solving planning problems, one of which is the use of domain ...
Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming ...
Declarative problem solving, such as planning, poses interestig challenges for Genetic Programming ...
This work investigates the application of Evolutionary Computation (EC) to the induction of generali...
There are several ways of applying Genetic Programming (GP) to STRIPS-like planning in the literat...
This paper presents a study of different methods of using incremental evolution with genetic program...
Recent experiments with a genetic-based encoding schema are presented as a potentially useful tool i...