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Table 4. Increasing the importance of the extra 25 xed strategies causes the co-evolutionary GA to produce strategies that are even more cooperative among themselves, but are still not exploited by the unseen strategies of the enumerative search.

in An Experimental Study of N-Person Iterated Prisoner's Dilemma Games
by Xin Yao, Paul J. Darwen 1994
Cited by 39

Table 4. Increasing the importance of the extra 25 fixed strategies causes the co-evolutionary GA to produce strategies that are even more cooperative among themselves, but are still not exploited by the unseen strategies of the enumerative search.

in An experimental study of N-person iterated prisoner's dilemma games
by Xin Yao, Paul J. Darwen 1994
Cited by 39

Table 4: Increasing the importance of the extra 25 xed strategies causes the co-evolutionary GA to produce strategies that are even more cooperative among themselves, but are still not exploited by the unseen strategies of the enumerative search.

in Genetic Algorithms and Evolutionary Games
by Xin Yao, Paul Darwen 2000
Cited by 1

Table 2: Summary of the coevolutionary Results Best Average

in Vehicle routing problem: Doing it the evolutionary way
by Penousal Machado, Jorge Tavares, Francisco B. Pereira, Ernesto Costa 2002
Cited by 7

Table 5.1: Fixed parameters of the co-evolutionary algorithm.

in Applying Adaptive Evolutionary Algorithms to Hard Problems
by J.I. van Hemert

Table 4 Parameter settings for the standard genetic algorithm.

in Abbreviated Title: 3D Container Loading Using a CCGA
by Chaiwat Pimpawat, Nachol Chaiyaratana, Dr. Nachol Chaiyaratana
"... In PAGE 17: ... The comparison between the search results will be used to identify the co-operative co-evolutionary effect on the algorithm performance. Note that the parameter settings for both approaches of the standard genetic algorithm search are summarised in Table4 . The simulation results from using the modified CCGA and the standard genetic algorithm to find a suitable loading sequence will be presented in the following section.... ..."

Table S- 1. Frequencies of Searches (all observations), March 2002-October 2002

in Table of Contents
by Nicholas Lovrich Ph. D, Michael Gaffney J. D, Clay Mosher Ph. D 2003

Table 5. Top 10 interacting partners predicted by the Single-Profile and Coevolutionary-Matrix methods for the E.coli KdpD

in BIOINFORMATICS ORIGINAL PAPER
by Yohan Kim, Mehmet Koyutürk, Umut Topkara, Ananth Grama, Shankar Subramaniam

Table 3. Best of Run pairwise comparison between the different components of the Pareto Archive when they are used as the Coevolutionary Memory. Using the whole archive is better (p lt; 0.05) than using the learners only.

in ACKNOWLEDGEMENTS
by German A. Monroy, German A. Monroy, Risto Miikkulainen, Bruce Porter 2005
"... In PAGE 9: ...he Pareto Archive, using the Pareto Archive as the Coevolutionary Memory. ................30 Table3 . Best of Run pairwise comparison between the different components of the Pareto Archive when they are used as the Coevolutionary Memory.... In PAGE 43: ...05) than using the learners only. The results of the Best of Run comparison methodology are shown in Table3 . The only statistically significant comparison is that it is better to use the whole archive than just the learners.... ..."

Table 1. Sucess rates and the corresponding AES values (within brackets) for the co-evolutionary GA (COE), the Micro-genetic algorithm with Iterative Descent (MID), and the SAW-ing GA (SAW)

in Solving Binary Constraint Satisfaction Problems using Evolutionary Algorithms with an Adaptive Fitness Function
by A. E. Eiben, J.I. van Hemert, E. Marchiori, A. G. Steenbeek 1998
"... In PAGE 6: ...Table 1. Sucess rates and the corresponding AES values (within brackets) for the co-evolutionary GA (COE), the Micro-genetic algorithm with Iterative Descent (MID), and the SAW-ing GA (SAW) Table1 summarizes the results of our experiments. Considering the success rate it is clear that MID performs equally or better than the other two EAs in all classes of instances.... In PAGE 7: ... Simple and unbiased as this decoder may seem, it could represent a successful heuristic. The results of the experiments reported in Table1 consider n = 15 variables and m = 15 domain size. It is interesting to investigate how the results scale up when we vary the number of variables n.... In PAGE 7: ... However, since the two curves are crossing at the end, we do not want to suggest a better scale-up behavior for either algorithms. Considering the results in Table1 from the point of view of the problem instances, we can observe success rates SR = 1 in the upper left corner, while SR = 0 is typical in the lower right corner, separated by a `diagonal apos; indicating the mushy region. Technically, for higher constraint density and tightness, all three EAs are unable to nd any solution.... ..."
Cited by 20
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