Experimentation is necessary - a purely theoretical approach is not reasonable. The new experimentalism, a development in the modern philosophy of science, considers that an experiment can have a life of its own. It provides a statistical methodology to learn from experiments, where the experimenter
Experimental Research in Evolutionary Computation
โ Scribed by Thomas Bartz-Beielstein
- Publisher
- Springer
- Year
- 2010
- Tongue
- English
- Leaves
- 220
- Series
- Natural Computing Series
- Edition
- 1st Edition.
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. It develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples.
๐ SIMILAR VOLUMES
<p><p>This is the ideal book for anyone contemplating starting a career in, or shifting their career to, studying the dynamics that drive cancer progression and its response to therapy. Topics include the theory and population genetics of cancers, genetic diversity within tumors (intra-tumor heterog
"This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC)"--</div> <br> Abstract: <div class="showMoreLessReadmore"> Introducing a handbook for gene regulatory network research using evolutionary computation, wit
<P>This book is loaded with examples in which computer scientists and engineers have used evolutionary computation - programs that mimic natural evolution - to solve real problems. They aren t abstract, mathematically intensive papers, but accounts of solving important problems, including tips from
Bioinformatics has never been as popular as it is today. The genomics revolution is generating so much data in such rapid succession that it has become difficult for biologists to decipher. In particular, there are many problems in biology that are too large to solve with standard methods. Researche