000			00652nam a2200205Ia 4500
999			_c14673 _d14673
008			161214s9999 xx 000 0 und d
020			_a9780387332543 _c(hbk)
082			_a005.1 _bCOE
100			_aLamont, Gary B.
245		0	_aEvolutionary algorithms for solving multi-objective problems
250			_a2nd ed.
260			_aNew York: _bSpringer, _c2007
300			_axxi, 800 p.; _bill.: _c24 cm.
365			_aINR _b4763.10
490			_aGenetic and eolutionary computation
520			_aThe solving of multi-objective problems (MOPs) has been a continuing effort by humans in many diverse areas, including computer science, engineering, economics, finance, industry, physics, chemistry, and ecology, among others. Many powerful and deterministic and stochastic techniques for solving these large dimensional optimization problems have risen out of operations research, decision science, engineering, computer science and other related disciplines. The explosion in computing power continues to arouse extraordinary interest in stochastic search algorithms that require high computational speed and very large memories. A generic stochastic approach is that of evolutionary algorithms (EA). Such algorithms have been demonstrated to be very powerful and generally applicable for solving different single objective problems. Their fundamental algorithmic structures can also be applied to solving many multi-objective problems. In this book, the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and unique fashion, with detailed customized forms suggested for a variety of applications. Also, extensive MOEA discussion questions and possible research directions are presented at the end of each chapter.
650			_aEvolutionary computation
650			_aEvolutionary programming
650			_aArtificial intelligence
650			_aInformation theory
650			_aMoea Testing and Analysis
650			_aMoea Theory
700			_aCoello Coello, Carlos A.
700			_aVan Veldhuizen, David A.
942			_2ddc _cBK