Cuckoo Search: From Cuckoo Reproduction Strategy to Combinatorial Optimization Aziz Ouaarab and Xin-She Yang Abstract Combinatorial optimization problems, specially those that are NP-hard, are increasingly being dealt with by stochastic, metaheuristic approaches. Most recently developed metaheuristics are nature-inspired and they are often inspired by some special characteristics in evolution, ecological or biological systems. This chapter discusses how to go from a biological phenomenon such as the aggres- sive reproduction strategy of cuckoos to solve tough problems in the combinatorial search space. Key features and steps are highlighted, together with the discussions of further research topics. Citation Detail: A. Ouaarab and X. S. Yang, Cuckoo Search: From Cuckoo Repro- duction Strategy to Combinatorial Optimization, in: Nature-Inspired Computation in Engineering (Ed. Xin-She Yang), Springer Studies in Computational Intelligence, Vol. 637, pp. 91-100 (2016). Published on 20 March 2016. 1 Introduction Many combinatorial optimization problems are non-deterministic polynomial-time hard (or NP-hard) and there are no efficient algorithms to solve such hard optimiza- tion problems. That is to say that there is no algorithm that can find its optimal so- Aziz Ouaarab LRIT, Associated Unit to the CNRST (URAC 29), Mohammed V-Agdal University, B.P. 1014 Rabat, Morocco, e-mail: aziz.ouaarab@gmail.com Xin-She Yang School of Science and Technology, Middlesex University, The Burroughs, London NW4 4BT, UK e-mail: x.yang@mdx.ac.uk 1