Learning to solve planning problems efficiently by means of genetic programming

Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator - Instance-Based Crossover - that is able to use traces of the base planner as raw genetic material to be injected into the evolving population.Publicad

Immediate transfer of global improvements to all individuals in a population compared to automatically defined functions for the EVEN-5,6-PARITY problems

Proceeding of: First European Workshop, EuroGP’98 Paris, France, April 14–15Koza has shown how automatically defined functions (ADFs) can reduce computational effort in the GP paradigm. In Koza’s ADF, as well as in standard GP, an improvement in a part of a program (an ADF or a main body) can only be transferred via crossover. In this article, we consider whether it is a good idea to transfer immediately improvements found by a single individual to the whole population. A system that implements this idea has been proposed and tested for the EVEN-5-PARITY and EVEN-6-PARITY problems. Results are very encouraging: computational effort is reduced (compared to Koza’s ADFs) and the system seems to be less prone to early stagnation. Finally, our work suggests further research where less extreme approaches to our idea could be tested

The Effects of Transfer of Global Improvements in Genetic Programming

Koza has shown how Automatically Defined Functions (ADFs) can reduce computational effort in the genetic programming paradigm. In Koza's Automatically Defined Functions, as well as in standard genetic programming, an improvement in a part of a program (an ADF or a main body) can only be transferred to other individuals in the population via crossover. In this article, we consider whether it is a good idea to transfer immediately improvements found by a single individual to other individuals in the population. A system that implements this idea has been proposed and tested for the even-5-parity, even-6-parity, and even-10-parity problems. Results are very encouraging: computational effort is reduced (compared to Koza's ADFs) and the system seems to be less prone to early stagnation. Also, as evolution occurs in separate populations, our approach permits to parallelize genetic programming in another different way

Evolving heuristics for planning

Abstract. In this paper we describe EvoCK, a new approach to the application of genetic programming (GP) to planning. This approach starts with a traditional AI planner (PRODIGY) and uses GP to acquire control rules to improve its efficiency. We also analyze two ways to introduce domain knowledge acquired by another method (HAMLET) into EvoCK: seeding the initial population and using a new operator (knowledge-based crossover). This operator combines genetic material from both an evolving population and a non-evolving population containing background knowledge. We tested these ideas in the blocksworld domain and obtained excellent results.