From the reviews: "[...]a highly interesting book recommendable to anyone interested in evolutionary optimization and to those facing noisy optimization problems." (Hans-Georg Beyer) "The book addresses one of the most pressing and interesting topics in evolutionary computation research - the performance of evolutional algorithms in uncertain environments ... . Summing up, the book appears to be an interesting theoretical complement to many existing books describing practical applications of evolutionary computations." (Jacek Blazewicz, Zentralblatt MATH, Vol. 1103 (5), 2007)

• 1. Introduction.- 2. Preliminaries.- 1 The Basic $$({\mu \mathord{\left/{\vphantom {\mu {\rho \mathop + \limits_, \lambda }}} \right.\kern-\nulldelimiterspace} {\rho \mathop + \limits_, \lambda }}) - ES$$.- 2 Mutation Strength Adaptation.- 3 Fitness Environments.- 4 Measuring Performance.- 5 Modeling the Sphere.- 3. The (1 + 1)-ES: Overvaluation.- 1 Overvaluation.- 2 Performance.- 3 Discussion.- 4. The (?,?)-ES: Distributed Populations.- 1 Modeling the Population.- 2 The Infinite Noise Limit.- 3 Finite Noise Strength.- 4 The Spherical Environment.- 5. The (?/?,?)-ES: Genetic Repair.- 1 Simple Performance Analysis.- 2 Improving the Accuracy.- 3 Cumulative Mutation Strength Adaptation.- 6. Comparing Approaches To Noisy Optimization.- 1 The Competitors.- 2 The Competition.- 7. Conclusions.- Appendices.- References.

From the reviews: "[...]a highly interesting book recommendable to anyone interested in evolutionary optimization and to those facing noisy optimization problems." (Hans-Georg Beyer) "The book addresses one of the most pressing and interesting topics in evolutionary computation research - the performance of evolutional algorithms in uncertain environments ... . Summing up, the book appears to be an interesting theoretical complement to many existing books describing practical applications of evolutionary computations." (Jacek Blazewicz, Zentralblatt MATH, Vol. 1103 (5), 2007)

• 1. Introduction.- 2. Preliminaries.- 1 The Basic $$({\mu \mathord{\left/{\vphantom {\mu {\rho \mathop + \limits_, \lambda }}} \right.\kern-\nulldelimiterspace} {\rho \mathop + \limits_, \lambda }}) - ES$$.- 2 Mutation Strength Adaptation.- 3 Fitness Environments.- 4 Measuring Performance.- 5 Modeling the Sphere.- 3. The (1 + 1)-ES: Overvaluation.- 1 Overvaluation.- 2 Performance.- 3 Discussion.- 4. The (?,?)-ES: Distributed Populations.- 1 Modeling the Population.- 2 The Infinite Noise Limit.- 3 Finite Noise Strength.- 4 The Spherical Environment.- 5. The (?/?,?)-ES: Genetic Repair.- 1 Simple Performance Analysis.- 2 Improving the Accuracy.- 3 Cumulative Mutation Strength Adaptation.- 6. Comparing Approaches To Noisy Optimization.- 1 The Competitors.- 2 The Competition.- 7. Conclusions.- Appendices.- References.

From the reviews: "[...]a highly interesting book recommendable to anyone interested in evolutionary optimization and to those facing noisy optimization problems." (Hans-Georg Beyer) "The book addresses one of the most pressing and interesting topics in evolutionary computation research - the performance of evolutional algorithms in uncertain environments ... . Summing up, the book appears to be an interesting theoretical complement to many existing books describing practical applications of evolutionary computations." (Jacek Blazewicz, Zentralblatt MATH, Vol. 1103 (5), 2007)

• 1. Introduction.- 2. Preliminaries.- 1 The Basic $$({\mu \mathord{\left/{\vphantom {\mu {\rho \mathop + \limits_, \lambda }}} \right.\kern-\nulldelimiterspace} {\rho \mathop + \limits_, \lambda }}) - ES$$.- 2 Mutation Strength Adaptation.- 3 Fitness Environments.- 4 Measuring Performance.- 5 Modeling the Sphere.- 3. The (1 + 1)-ES: Overvaluation.- 1 Overvaluation.- 2 Performance.- 3 Discussion.- 4. The (?,?)-ES: Distributed Populations.- 1 Modeling the Population.- 2 The Infinite Noise Limit.- 3 Finite Noise Strength.- 4 The Spherical Environment.- 5. The (?/?,?)-ES: Genetic Repair.- 1 Simple Performance Analysis.- 2 Improving the Accuracy.- 3 Cumulative Mutation Strength Adaptation.- 6. Comparing Approaches To Noisy Optimization.- 1 The Competitors.- 2 The Competition.- 7. Conclusions.- Appendices.- References.