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by Edmund Burke, Steven Gustafson, Graham Kendall
http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-2002.ps
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Abstract:
corresponding author Abstract. This report represents an initial investigation into the use of genetic programming to solve the N-prisoners puzzle. The puzzle has generated a certain level of interest among the mathematical community. We believe that this puzzle presents a signicant challenge to the eld of evolutionary computation and to genetic programming in particular. The overall aim is to generate a solution that encodes complex decision making. Our initial results demonstrate that genetic programming can evolve good solutions. We compare these results to engineered solutions and discuss some of the implications. One of the consequences of this study is that it has highlighted a number of research issues and directions and challenges for the evolutionary computation community. We conclude the article by presenting some of these directions which range over several areas of evolutionary computation, including multi-objective tness, coevolution and cooperation, and problem representations. 1
Citations
|
1782
|
Genetic Programming: On the Programming of Computers by Means of Natural Selection Cambridge
– Koza
- 1992
|
|
119
|
W.: "Coding and Information Theory
– Hamming
- 1980
|
|
50
|
Genetic programming produced competitive soccer softbot teams for robocup97
– Luke
- 1998
|
|
40
|
Evolving team Darwin United
– Andre, Teller
- 1999
|
|
26
|
The royal tree problem, a benchmark for single and multiple population genetic programming
– Punch, Zongker, et al.
- 1996
|
|
23
|
Issues in Scaling Genetic Programming: Breeding Strategies, Tree Generation, and Code Bloat
– Luke
- 2000
|
|
20
|
What makes a problem GP-hard? analysis of a tunably difficult problem in genetic programming. Genetic Programming and Evolvable Machines
– Daida
- 2001
|
|
19
|
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
– Knowles, Watson, et al.
- 2001
|
|
18
|
Data Structures and Genetic Programming: Genetic Programming + Data Structures = Automatic Programming!, volume 1 of Genetic Programming
– Langdon
- 1998
|
|
14
|
Applications of Recursive Operators to Randomness and Complexity
– Ebert
- 1998
|
|
13
|
Using genetic programming to approximate maximum clique
– Soule, Foster, et al.
- 1996
|
|
10
|
On the Autoreducibility of Random Sequences
– Ebert, Merkle, et al.
|
|
9
|
Why mathematicians now care about their hat color
– Robinson
- 2001
|
|
6
|
Using programmatic motifs and genetic programming to classify protein sequences as to extracellular and membrane cellular location
– Koza, Bennett, et al.
- 1998
|
|
6
|
Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem
– Gustafson, Hsu
- 2001
|