@article{737578,
      recid = {737578},
      author = {Preuss, Mike,},
      title = {Multimodal optimization by means of evolutionary  algorithms [electronic resource] /},
      pages = {1 online resource.},
      abstract = {This book offers the first comprehensive taxonomy for  multimodal optimization algorithms, work with its root in  topics such as niching, parallel evolutionary algorithms,  and global optimization. The author explains niching in  evolutionary algorithms and its benefits; he examines their  suitability for use as diagnostic tools for experimental  analysis, especially for detecting problem (type)  properties; and he measures and compares the performances  of niching and canonical EAs using different benchmark test  problem sets. His work consolidates the recent successes in  this domain, presenting and explaining use cases,  algorithms, and performance measures, with a focus  throughout on the goals of the optimization processes and a  deep understanding of the algorithms used. The book will be  useful for researchers and practitioners in the area of  computational intelligence, particularly those engaged with  heuristic search, multimodal optimization, evolutionary  computing, and experimental analysis.},
      url = {http://library.usi.edu/record/737578},
}