@article {40,
	title = {Seeking Multiple Solutions: an Updated Survey on Niching Methods and Their Applications},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {PP},
	year = {2016},
	pages = {1-1},
	abstract = {Multi-Modal Optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specificallydesigned diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multi-objective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving.},
	keywords = {Benchmark testing, evolutionary computation, Meta-heuristics, Multi-modal optimization, Multi-solution methods, Niching methods, Optimization methods, Problem-solving, Sociology, Statistics, Swarm intelligence, Two dimensional displays},
	issn = {1089-778X},
	doi = {10.1109/TEVC.2016.2638437},
	author = {X. Li and M. G. Epitropakis and K. Deb and A. Engelbrecht}
}
@article {6,
	title = {Studying the basin of convergence of methods for computing periodic orbits},
	journal = {International Journal of Bifurcation and Chaos (IJBC)},
	volume = {21},
	year = {2011},
	pages = {1-28},
	abstract = {Starting from the well-known Newton{\textquoteright}s fractal which is formed by the basin of convergence of Newton{\textquoteright}s method applied to a cubic equation in one variable in the field ℂ, we were able to find methods for which the corresponding basins of convergence do not exhibit a fractal-like structure. Using this approach we are able to distinguish reliable and robust methods for tackling a specific problem. Also, our approach is illustrated here for methods for computing periodic orbits of nonlinear mappings as well as for fixed points of the Poincar{\'e} map on a surface of section.},
	doi = {10.1142/S0218127411029653},
	author = {M. G. Epitropakis and M. N. Vrahatis}
}
