Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy...
Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the success...
Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu- tionary search, into evolutionary algorithms has received increasing interest in the recent years. It has been shown from various motivations that knowl- edge incorporation into evolutionary search is able to significantly improve search efficiency. However, results on knowledge incorporation in evolution- ary computation have been scattered in a wide range of research areas and a systematic handling of this important topic in evolutionary...
Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evol...
Biological systems are inherently stochastic and uncertain. Thus, research in bioinformatics, biomedical engineering and computational biology has to deal with a large amount of uncertainties.
Fuzzy logic has shown to be a powerful tool in capturing different uncertainties in engineering systems. In recent years, fuzzy logic based modeling and analysis approaches are also becoming popular in analyzing biological data and modeling biological systems. Numerous research and application results have been reported that demonstrated the effectiveness of fuzzy logic in solving a wide range...
Biological systems are inherently stochastic and uncertain. Thus, research in bioinformatics, biomedical engineering and computational biology has ...
Evolutionary computation is a class of problem optimization methodology with the inspiration from the natural evolution of species. In nature, the population of a species evolves by means of selection and variation. These two principles of natural evolution form the fundamental of evolutionary - gorithms (EAs). During the past several decades, EAs have been extensively studied by the computer science and arti?cial intelligence communities. As a classofstochasticoptimizationtechniques, EAscanoftenoutperformclassical optimization techniques for di?cult real world problems. Due to the ease of...
Evolutionary computation is a class of problem optimization methodology with the inspiration from the natural evolution of species. In nature, the pop...
Fuzzy rule systems have found a wide range of applications in many fields of science and technology. Traditionally, fuzzy rules are generated from human expert knowledge or human heuristics for relatively simple systems. In the last few years, data-driven fuzzy rule generation has been very active. Compared to heuristic fuzzy rules, fuzzy rules generated from data are able to extract more profound knowledge for more complex systems. This book presents a number of approaches to the generation of fuzzy rules from data, ranging from the direct fuzzy inference based to neural net- works and...
Fuzzy rule systems have found a wide range of applications in many fields of science and technology. Traditionally, fuzzy rules are generated from hum...
Evolutionary computation is a class of problem optimization methodology with the inspiration from the natural evolution of species. In nature, the population of a species evolves by means of selection and variation. These two principles of natural evolution form the fundamental of evolutionary - gorithms (EAs). During the past several decades, EAs have been extensively studied by the computer science and arti?cial intelligence communities. As a classofstochasticoptimizationtechniques, EAscanoftenoutperformclassical optimization techniques for di?cult real world problems. Due to the ease of...
Evolutionary computation is a class of problem optimization methodology with the inspiration from the natural evolution of species. In nature, the pop...
Biological systems are inherently stochastic and uncertain. Thus, research in bioinformatics, biomedical engineering and computational biology has to deal with a large amount of uncertainties.
Fuzzy logic has shown to be a powerful tool in capturing different uncertainties in engineering systems. In recent years, fuzzy logic based modeling and analysis approaches are also becoming popular in analyzing biological data and modeling biological systems. Numerous research and application results have been reported that demonstrated the effectiveness of fuzzy logic in solving a wide range...
Biological systems are inherently stochastic and uncertain. Thus, research in bioinformatics, biomedical engineering and computational biology has ...
Inspired by biological phenomena such as social insect behavior and molecular morphogenesis, self-organizing approaches in advanced robotic systems have an ever-higher profile, and this valuable reference covers the state of the art in this field.
Inspired by biological phenomena such as social insect behavior and molecular morphogenesis, self-organizing approaches in advanced robotic systems ha...
