@techreport{2005005,
abstract = {Most optimization problems have constraints of different types (e.g., physical, time, geometric, etc.) which modify the shape of the search space. During the last few years, a wide variety of metaheuristics have been designed and applied to solve constrained optimization problems. Evolutionary algorithms and most other metaheuristics, when used for optimization, naturally operate as unconstrained search techniques. Therefore, they require an additional mechanism to incorporate constraints into their fitness function. Historically, the most common approach to incorporate constraints (both in evolutionary algorithms and in mathematical programming) is the penalty functions, which were originally proposed in the 1940s and later expanded by many researchers. Penalty functions have, in general, several limitations. Particularly, they are not a very good choice when trying to solve problem in which the optimum lies in the boundary between the feasible and the infeasible regions or when the feasible region is disjoint. Additionally, penalty functions require a careful fine-tuning to determine the most appropriate penalty factors to be used with our metaheuristics. In order to overcome the limitations of penalty functions approach, researchers have proposed a number of diverse approaches to handle constraints such as fitness approximation in constrained optimization, incorporation of knowledge such as cultural approaches in constrained optimization and so on. Additionally, the analysis of the role of the search engine has also become an interesting research topic in the last few years. For example, evolution strategies (ES), evolutionary programming (EP), differential evolution (DE) and particle swarm optimization (PSO) have been found advantageous by some researchers over other metaheuristics such as the binary genetic algorithms (GA). In this report, 24 benchmark functions are described and guidelines for conducting experiments with performance evaluation criteria are given. The code which could be employed by C/C++/C{\#}, Matlab, Java for them could be found at http://www.ntu.edu.sg/home/EPNSugan/. The mathemat-ical formulas and properties of these functions are described in Section 1. In Section 2, the evaluation criteria are given. And a suggested results format is given in Section 3.},
author = {Suganthan, Ponnuthurai N and Hansen, Nikolaus and Liang, Jing J and Deb, Kalyanmoy and Chen, Y. -P and Auger, Anne and Tiwari, S},
title = {{Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization}},
institution={Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology Kanpur, Kalyanpur, Kanpur, Uttar Pradesh 208016, India},
number={2005005},
url = {https://coin-lab.org/content/publications.html},
year = {2005}
}