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ABSTRACT: Mind Evolutionary Computation (MEC) is a new approach to evolutionary computation (EC). It is shown that MEC has a much higher computing efficiency and convergence ability than genetic algorithms (GA). A novel method of analyzing similartaxis process is presented. We obtained the relation between the calculated amount in similartaxis and the parameters of MEC, including the parameters of a probability density function for scattering individuals, the size of group, the precision of solution and the distance between initial searching position and local optimum with this method. Experimentation shows that the analysis result agrees with the experimental data and the analysis method is correct. The experiment also analyzes the influence of different sizes of groups on searching efficiency and a reasonable range of the size of groups is achieved. The analysis can also be used to direct the improvement of MEC performance. To sum up, this analyzing method is reasonable, feasible and directive
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th; 08/2001
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ABSTRACT: Mind evolutionary computation (MEC) is a new approach to
evolutionary computation (EC). This paper presents a new dissimilation
strategy using rejected regions, which can avoid searching repeatedly,
so that the capability of MEC to search globally in dissimilation is
enhanced. Experimental results show that basic MEC has improved
considerately compared with a genetic algorithm (GA), and that the MEC
dissimilation strategy using rejected regions has also advanced a lot.
The reason for this is that, in the modified MEC, the regions searched
in similartaxis are recorded, so that, in dissimilation, the scope of
scattered individuals is reduced to the whole solution space, excluding
the rejected regions. Therefore, the regions explored in dissimilation
have never been searched before, and the search scope is diminished
accordingly, while the capability of MEC to search globally in
dissimilation is enhanced and repeated searching is avoided. It is the
memory mechanism of MEC that makes the dissimilation strategy of
rejected regions possible, so the probability that the individuals are
scattered in the region of the global optimum has greatly increased, the
calculated amount and the average evaluation time are decreased, and
population convergence can be implemented in fewer generations
Systems, Man, and Cybernetics, 2001 IEEE International Conference on; 02/2001
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[show abstract]
[hide abstract]
ABSTRACT: Mind evolutionary computation (MEC) is a new approach to
evolutionary computation (EC). It is proved that MEC has much higher
computational efficiency and convergence ability than genetic algorithms
(GAs). This is because MEC uses the operations of similartaxis and
dissimilation rather than the crossover and mutation operators used in
GAs, and also because of the different performance mechanisms from GAs,
the memory mechanism, the evolutionary directional mechanism and the
harmonizing mechanism between exploitation and exploration. This paper
presents a new dissimilation strategy using rejected regions, which can
avoid searching repeatedly, so that the capability of MEC to search
globally in dissimilation is enhanced. It is the memory mechanism of MEC
that makes the dissimilation strategy of rejected regions possible
Systems, Man, and Cybernetics, 2001 IEEE International Conference on; 02/2001
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[show abstract]
[hide abstract]
ABSTRACT: Mind evolutionary computation (MEC) is a new approach of
evolutionary computation (EC). It is proved that MEC has much higher
computing efficiency and convergence ability than genetic algorithms
(GAs). This is because of using operation similartaxis and dissimilation
rather than crossover and mutation operators in GA. The paper analyzes
the influence of type of the probability density function on
similartaxis in MEC. We get theoretically the relation among
similartaxis calculated amount, the parameters of probability density
function of scattering individuals, the size of group, the precision of
solution and the distance between initial searching position and local
optimum. The experiment shows that the analysis method proposed in the
paper is reasonable. The analysis and experiment also shows that using
different types of probability density functions doesn't make much
change on similartaxis searching performance
Fuzzy Systems, 2001. The 10th IEEE International Conference on; 02/2001
