Titlebook: Nature Inspired Cooperative Strategies for Optimization (NICSO 2013); Learning, Optimizati German Terrazas,Fernando E. B. Otero,Antonio D.

半圆凿

聋子

模仿

amphibian

Fitness Based Self Adaptive Differential Evolution, named as Fitness based Self Adaptive DE (.). The experiments on 16 well known test problems of different complexities show that the proposed strategy outperforms the basic DE and recent variants of DE, namely Self-adaptive DE (.) and Scale Factor Local Search DE (.) in most of the experiments.

NEG

A Cooperative Approach Using Ants and Bees for the Graph Coloring Problem,S1 (construction strategy) gives best results and is quite fast compared to other methods. Moreover, the parallel implementation of ACS reduces significantly the execution time. Finally, we show that the cooperation between ACS and MBO improves the results obtained separately by each algorithm.

Ptsd429

Artificial Bee Colony Training of Neural Networks,pplied, we conclude that the ABC approach does perform very well on small problems, but the generalization performances achieved are only significantly better than standard BP on one out of six datasets, and the training times increase rapidly as the size of the problem grows.

返老还童

Nonlinear Optimization in Landscapes with Planar Regions,for descendent functions and more exploration for planar functions. Preliminary results show that the proposed hybrid algorithm finds better results than PSO and Monte Carlo techniques in isolation for ten well-known test functions.

精密

Optimizing Neighbourhood Distances for a Variant of Fully-Informed Particle Swarm Algorithm, Swarm Optimization algorithm to successfully solve a problem. These configurations are often contrary to what people would design using their intuitions. This means that meta-optimization in this case can be used as a tool for scientific exploration as well as for practical utility.

Petechiae

Response Surfaces with Discounted Information for Global Optima Tracking in Dynamic Environments, after a given number of function evaluations with as few samples as possible. Exploiting old information in a discounted manner significantly improves the search, which is shown through numerical experiments performed using the moving peaks benchmark (MPB).

白杨鱼

Using Base Position Errors in an Entropy-Based Evaluation Function for the Study of Genetic Code Adndicate that, when the proposed evaluation function is compared to the standard evaluation function based only on robustness, the difference between the fitness of the best hypothetical codes found by the GA and the fitness of the canonical genetic code is smaller.