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 Postdoctoral Research Fellow in the School of Computer Science at the University of Birmingham; he previously held a position at 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 Postdoctoral Research Fellow in the School of Computer Science at the University of Birmingham; he previously held a position at 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 Postdoctoral Research Fellow in the School of Computer Science at the University of Birmingham; he previously held a position at 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.