Evolutionary Computations New Algorithms And Their Applications to Evolutionary Robots

Swarm intelligence algorithms represent a rapidly growing research domain and have recently attracted a great deal of attention. They have been successfully applied in engineering, transportation, planning and scheduling, logistics and supply chains, and a broad range of other domains. They often find high-quality solutions with less computational effort than other optimization methods.

This book is about problem solving. Specifically, it is about heuristic state-space search under branch-and-bound framework for solving com­ binatorial optimization problems. The two central themes of this book are the average-case complexity of heuristic state-space search algorithms based on branch-and-bound, and their applications to developing new problem-solving methods and algorithms.

Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This course is an introduction to algorithms for learners with at least a little programming experience. The course is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this course, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.

A great way learn to how to solve real problems using Winforms in C#. More videos and exercises than any Intro course.

Data Mining and Applications in Genomics contains the data mining algorithms and their applications in genomics, with frontier case studies based on the recent and current works at the University of Hong Kong and the Oxford University Computing Laboratory, University of Oxford. It provides a systematic introduction to the use of data mining algorithms as an investigative tool for applications in genomics. Topics covered include Genomic Techniques, Single Nucleotide Polymorphisms, Disease Studies, HapMap Project, Haplotypes, Tag-SNP Selection, Linkage Disequilibrium Map, Gene Regulatory Networks, Dimension Reduction, Feature Selection, Feature Extraction, Principal Component Analysis, Independent Component Analysis, Machine Learning Algorithms, Hybrid Intelligent Techniques, Clustering Algorithms, Graph Algorithms, Numerical Optimization Algorithms, Data Mining Software Comparison, Medical Case Studies, Bioinformatics Projects, and Medical Applications.