Dissertation/ Thesis
Rapid development of problem-solvers with HeurEAKA! - a heuristic evolutionary algorithm and incremental knowledge acquisition approach
| Title: | Rapid development of problem-solvers with HeurEAKA! - a heuristic evolutionary algorithm and incremental knowledge acquisition approach |
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| Authors: | Bekmann, Joachim Peter |
| Publisher Information: | UNSW, Sydney |
| Publication Year: | 2006 |
| Collection: | UNSW Sydney (The University of New South Wales): UNSWorks |
| Subject Terms: | Problem solving - Computer programs, Artificial intelligence, Traffic lights - Mathematical models |
| Description: | A new approach for the development of problem-solvers for combinatorial problems is proposed in this thesis. The approach combines incremental knowledge acquisition and probabilistic search algorithms, such as evolutionary algorithms, to allow a human to rapidly develop problem-solvers in new domains in a framework called HeurEAKA. The approach addresses a known problem, that is, adapting evolutionary algorithms to the search domain by the introduction of domain knowledge. The development of specialised problem-solvers has historically been labour intensive. Implementing a problem-solver from scratch is very time consuming. Another approach is to adapt a general purpose search strategy to the problem domain. This is motivated by the observation that in order to scale an algorithm to solve complex problems, domain knowledge is needed. At present there is no systematic approach allowing one to efficiently engineer a specialpurpose search strategy for a given search problem. This means that, for example, adapting evolutionary algorithms (which are general purpose algorithms) is often very difficult and has lead some people to refer to their use as a “black art”. In the HeurEAKA approach, domain knowledge is introduced by incrementally building a knowledge base that controls parts of the evolutionary algorithm. For example, the fitness function and the mutation operators in a genetic algorithm. An evolutionary search algorithm ismonitored by a human whomakes recommendations on search strategy based on individual solution candidates. It is assumed that the human has a reasonable intuition of the search problem. The human adds rules to a knowledge base describing how candidate solutions can be improved, or why they are desirable or undesirable in the search for a good solution. The incremental knowledge acquisition approach is inspired by the idea of (Nested) Ripple Down Rules. This approach sees a human provide exception rules to rules already existing in the knowledge base using concrete examples of inappropriate ... |
| Document Type: | doctoral or postdoctoral thesis |
| File Description: | application/pdf |
| Language: | English |
| Relation: | https://hdl.handle.net/1959.4/25748; https://doi.org/10.26190/unsworks/15644 |
| DOI: | 10.26190/unsworks/15644 |
| Availability: | https://hdl.handle.net/1959.4/25748 https://unsworks.unsw.edu.au/bitstreams/2f901ff4-0032-4072-afce-85e949bf6266/download https://doi.org/10.26190/unsworks/15644 |
| Rights: | open access ; https://purl.org/coar/access_right/c_abf2 ; CC BY-NC-ND 3.0 ; https://creativecommons.org/licenses/by-nc-nd/3.0/au/ ; free_to_read |
| Accession Number: | edsbas.2B58C311 |
| Database: | BASE |
| DOI: | 10.26190/unsworks/15644 |
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