الصفحة 1
الصفحة 1
Multiobjective Optimization : Interactive and Evolutionary Approaches
Multiobjective optimization deals with solving problems having not only one, but multiple, often conflicting, criteria. Such problems can arise in practically every field of science, engineering and business, and the need for efficient and reliable solution methods is increasing. The task is challenging due to the fact that, instead of a single optimal solution, multiobjective optimization results in a number of solutions with different trade-offs among criteria, also known as Pareto optimal or efficient solutions. Hence, a decision maker is needed to provide additional preference information and to identify the most satisfactory solution. Depending on the paradigm used, such information may be introduced before, during, or after the optimization process. Clearly, research and application in multiobjective optimization involve expertise in optimization as well as in decision support.
Mobile Robots : The Evolutionary Approach
The design and control of autonomous intelligent mobile robotic systems operating in unstructured changing environments includes many objective difficulties. There are several studies about the ways in which, robots exhibiting some degree of autonomy, adapt themselves to fit in their environments. The application and use of bio-inspired and intelligent techniques such as reinforcement learning, artificial neural networks, evolutionary computation and so forth in the design and improvement of robot designs is an emergent research topic. Researchers have obtained robots that display an amazing slew of behaviours and perform a multitude of tasks. These include perception of environment, planning and navigation in rough terrain, pushing boxes, negotiating an obstacle course, etc.
Introduction to Computational Biology : An Evolutionary Approach
Molecular biology has changed dramatically over the past two decades. Until the early 1990s genes were studied one at a time by small teams of researchers; today entire genomes are sequenced by internationally collaborating laboratories. In the bygone gene-centered era the accumulation of data was the rate-limiting step in research. Now that step is often data interpretation. This is increasingly dependent on computational methods and as a consequence, computational biology has emerged in the past decade as a new subdiscipline of biology. This introduction to computational biology is centered on the analysis of molecular sequence data. There are two closely connected aspects to biological sequences: (i) their relative position in the space of all other sequences, and (ii) their movement through this sequence space in evolutionary time. Accordingly, the first part of the book deals with classical methods of sequence analysis: pairwise alignment, exact string matching, multiple alignment, and hidden Markov models. In the second part evolutionary time takes center stage and phylogenetic reconstruction, the analysis of sequence variation, and the dynamics of genes in populations are explained in detail. In addition, the book contains a computer program with a graphical user interface that allows the reader to experiment with a number of key concepts developed by the authors.
Innovations Towards Sustainability : Conditions and Consequences
The volume contains eight articles together with comments by twenty authors and discussants on the topic of innovations and sustainability. It provides a competently written, balanced and differentiated state-of-the-art insight into the relation between innovations and sustainability from the perspective of evolutionary economics. The scope of the contributions encompasses the technological, social, organizational, and political dimensions of the topic. Each article is discussed by a competently written commentary providing a critical evaluation and relating it to the relevant literature. Particular interest lies on the issues of steering opportunities and path formation capabilities by decentralized agents, or governmental institutions from the viewpoint of evolutionary economics.
Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing : An Evolutionary Approach for Neural Networks and Fuzzy Systems
This monograph describes new methods for intelligent pattern recognition using soft computing techniques including neural networks, fuzzy logic, and genetic algorithms. Hybrid intelligent systems that combine several soft computing techniques are needed due to the complexity of pattern recognition problems.
Evolution of Non-Expected Utility Preferences
The theory on the evolution of preferences deals with the endogenous formation of preference relations in strategic situations. In particular, we demonstrate that preferences which diverge from von Neumann-Morgenstern expected utility may potentially prove to be successful under evolutionary pressures.
Encyclopedia of Cognitive Behavior Therapy
One of the hallmarks of cognitive behavior therapy is its diversity today. Since its inception, over twenty five years ago, this once revolutionary approach to psychotherapy has grown to encompass treatments across the full range of psychological disorders. The Encyclopedia of Cognitive Behavior Therapy brings together all of the key aspects of this field distilling decades of clinical wisdom into one authoritative volume
