Dr. Yossi Borenstein is the head of risk analytics at the company VisualDNA; he previously held a position at the University of Hertfordshire, and he received his PhD from the University of Essex; his research interests include data analysis, information retrieval, stochastic optimization, artificial intelligence, and evolutionary computation. Dr. Alberto Moraglio is a lecturer in the Dept. of Computer Science of the University of Exeter. He previously held positions at the University of Birmingham and the University of Coimbra, and he received his PhD from the University of Essex. His research focus is the theory of evolutionary computation.

From the book reviews: "This is a valuable addition to the literature on heuristics for search. Both practitioners and theoreticians should read it." (J. P. E. Hodgson, Computing Reviews, July, 2014)

• No Free Lunch Theorems: Limitations and Perspectives of Metaheuristics.- Convergence Rates of Evolutionary Algorithms and Parallel Evolutionary Algorithms.- Rugged and Elementary Landscapes.- Single-Funnel and Multi-funnel Landscapes and Subthreshold Seeking Behavior.- Black-Box Complexity for Bounding the Performance of Randomized Search Heuristics.- Designing an Optimal Search Algorithm with Respect to Prior Information.- The Bayesian Search Game.- Principled Design of Continuous Stochastic Search: From Theory to Practice.- Parsimony Pressure Made Easy: Solving the Problem of Bloat in GP.- Experimental Analysis of Optimization Algorithms: Tuning and Beyond.- Formal Search Algorithms + Problem Characterizations = Executable Search Strategies.

Dr. Yossi Borenstein is the head of risk analytics at the company VisualDNA; he previously held a position at the University of Hertfordshire, and he received his PhD from the University of Essex; his research interests include data analysis, information retrieval, stochastic optimization, artificial intelligence, and evolutionary computation. Dr. Alberto Moraglio is a lecturer in the Dept. of Computer Science of the University of Exeter. He previously held positions at the University of Birmingham and the University of Coimbra, and he received his PhD from the University of Essex. His research focus is the theory of evolutionary computation.

From the book reviews: "This is a valuable addition to the literature on heuristics for search. Both practitioners and theoreticians should read it." (J. P. E. Hodgson, Computing Reviews, July, 2014)

• No Free Lunch Theorems: Limitations and Perspectives of Metaheuristics.- Convergence Rates of Evolutionary Algorithms and Parallel Evolutionary Algorithms.- Rugged and Elementary Landscapes.- Single-Funnel and Multi-funnel Landscapes and Subthreshold Seeking Behavior.- Black-Box Complexity for Bounding the Performance of Randomized Search Heuristics.- Designing an Optimal Search Algorithm with Respect to Prior Information.- The Bayesian Search Game.- Principled Design of Continuous Stochastic Search: From Theory to Practice.- Parsimony Pressure Made Easy: Solving the Problem of Bloat in GP.- Experimental Analysis of Optimization Algorithms: Tuning and Beyond.- Formal Search Algorithms + Problem Characterizations = Executable Search Strategies.

Dr. Yossi Borenstein is the head of risk analytics at the company VisualDNA; he previously held a position at the University of Hertfordshire, and he received his PhD from the University of Essex; his research interests include data analysis, information retrieval, stochastic optimization, artificial intelligence, and evolutionary computation. Dr. Alberto Moraglio is a lecturer in the Dept. of Computer Science of the University of Exeter. He previously held positions at the University of Birmingham and the University of Coimbra, and he received his PhD from the University of Essex. His research focus is the theory of evolutionary computation.

From the book reviews: "This is a valuable addition to the literature on heuristics for search. Both practitioners and theoreticians should read it." (J. P. E. Hodgson, Computing Reviews, July, 2014)

• No Free Lunch Theorems: Limitations and Perspectives of Metaheuristics.- Convergence Rates of Evolutionary Algorithms and Parallel Evolutionary Algorithms.- Rugged and Elementary Landscapes.- Single-Funnel and Multi-funnel Landscapes and Subthreshold Seeking Behavior.- Black-Box Complexity for Bounding the Performance of Randomized Search Heuristics.- Designing an Optimal Search Algorithm with Respect to Prior Information.- The Bayesian Search Game.- Principled Design of Continuous Stochastic Search: From Theory to Practice.- Parsimony Pressure Made Easy: Solving the Problem of Bloat in GP.- Experimental Analysis of Optimization Algorithms: Tuning and Beyond.- Formal Search Algorithms + Problem Characterizations = Executable Search Strategies.