Cooperative co-evolution of GA-based classifiers based on input increments

Genetic algorithms (GAs) have been widely used as soft computing techniques in various applications, while cooperative co-evolution algorithms were proposed in the literature to improve the performance of basic GAs. In this paper, a new cooperative co-evolution algorithm, namely ECCGA, is proposed in the application domain of pattern classification. Concurrent local and global evolution and conclusive global evolution are proposed to improve further the classification performance. Different approaches of ECCGA are evaluated on benchmark classification data sets, and the results show that ECCGA can achieve better performance than the cooperative co-evolution genetic algorithm and normal GA. Some analysis and discussions on ECCGA and possible improvement are also presented

Fitness sharing and niching methods revisited

Interest in multimodal optimization function is expanding rapidly since real-world optimization problems often require the location of multiple optima in the search space. In this context, fitness sharing has been used widely to maintain population diversity and permit the investigation of many peaks in the feasible domain. This paper reviews various strategies of sharing and proposes new recombination schemes to improve its efficiency. Some empirical results are presented for high and a limited number of fitness function evaluations. Finally, the study compares the sharing method with other niching techniques

Sustainable Cooperative Coevolution with a Multi-Armed Bandit

This paper proposes a self-adaptation mechanism to manage the resources allocated to the different species comprising a cooperative coevolutionary algorithm. The proposed approach relies on a dynamic extension to the well-known multi-armed bandit framework. At each iteration, the dynamic multi-armed bandit makes a decision on which species to evolve for a generation, using the history of progress made by the different species to guide the decisions. We show experimentally, on a benchmark and a real-world problem, that evolving the different populations at different paces allows not only to identify solutions more rapidly, but also improves the capacity of cooperative coevolution to solve more complex problems.Comment: Accepted at GECCO 201

A Simple Cellular Automaton Model for Influenza A Viral Infections

Viral kinetics have been extensively studied in the past through the use of spatially homogeneous ordinary differential equations describing the time evolution of the diseased state. However, spatial characteristics such as localized populations of dead cells might adversely affect the spread of infection, similar to the manner in which a counter-fire can stop a forest fire from spreading. In order to investigate the influence of spatial heterogeneities on viral spread, a simple 2-D cellular automaton (CA) model of a viral infection has been developed. In this initial phase of the investigation, the CA model is validated against clinical immunological data for uncomplicated influenza A infections. Our results will be shown and discussed.Comment: LaTeX, 12 pages, 18 EPS figures, uses document class ReTeX4, and packages amsmath and SIunit

Methods for evolving robust programs

Abstract. Many evolutionary computation search spaces require fitness assessment through the sampling of and generalization over a large set of possible cases as input. Such spaces seem particularly apropos to Genetic Programming, which notionally searches for computer algorithms and functions. Most existing research in this area uses ad-hoc approaches to the sampling task, guided more by intuition than understanding. In this initial investigation, we compare six approaches to sampling large training case sets in the context of genetic programming representations. These approaches include fixed and random samples, and adaptive methods such as coevolution or fitness sharing. Our results suggest that certain domain features may lead to the preference of one approach to generalization over others. In particular, coevolution methods are strongly domain-dependent. We conclude the paper with suggestions for further investigations to shed more light onto how one might adjust fitness assessment to make various methods more effective.

Pattern Classification Using A Fuzzy Immune Network Model

It is generally believed that one major function of immune system is helping to protect multicellular organisms from foreign pathogens, especially replicating pathogens such as viruses, bacteria and parasites. The relevant events in immune system are not only the molecules, but also their interactions. The immune cells can respond either positively or negatively to the recognition signal. A positive response would result in cell proliferation, activation and antibody secretion, while a negative response would lead to tolerance and suppression. Depending upon these immune mechanisms, an immune network model(here, we call it the binary model) based on biological immune response network was proposed in our previous work. However, there are some problems like input and memory in the binary model. In order to improve the binary model, in this paper we propose a fuzzy immune network model. In the proposed fuzzy immune model, we add a normalization B cell layer for normalizing the large-scale antigen information on the base of the binary model. Meanwhile, a fuzzy AND operator(.AND.) and a normalization procedure called complement coding were employed in the proposed fuzzy immune model. Compute simulations illustrate that the proposed fuzzy model not only can improve the problems existing in the binary model but also is capable of clustering arbitrary sequences of large-scale analog input patterns into stable recognition categories. (author abst.

A new selection operator for genetic algorithms that balances between premature convergence and population diversity

The research objective is to find a balance between premature convergence and population diversity with respect to genetic algorithms (GAs). We propose a new selection scheme, namely, split-based selection (SBS) for GAs that ensures a fine balance between two extremes, i.e. exploration and exploitation. The proposed selection operator is further compared with five commonly used existing selection operators. A rigorous simulation-based investigation is conducted to explore the statistical characteristics of the proposed procedure. Furthermore, performance evaluation of the proposed scheme with respect to competing methodologies is carried out by considering 14 diverse benchmarks from the library of the traveling salesman problem (TSPLIB). Based on t-test statistic and performance index (PI), this study demonstrates a superior performance of the proposed scheme while maintaining the desirable statistical characteristics