@proceedings {29,
	title = {A Dynamic Archive Niching Differential Evolution Algorithm for Multimodal Optimization},
	year = {2013},
	month = {June},
	pages = {79-86},
	address = {Cancun, Mexico},
	abstract = {Highly multimodal landscapes with multiple local/global optima represent common characteristics in real-world applications. Many niching algorithms have been proposed in the literature which aim to search such landscapes in an attempt to locate as many global optima as possible. However, to locate and maintain a large number of global solutions, these algorithms are substantially influenced by their parameter values, such as a large population size. Here, we propose a new niching Differential Evolution algorithm that attempts to overcome the population size influence and produce good performance almost independently of its population size. To this end, we incorporate two mechanisms into the algorithm: a control parameter adaptation technique and an external dynamic archive along with a reinitialization mechanism. The first mechanism is designed to efficiently adapt the control parameters of the algorithm, whilst the second one is responsible for enabling the algorithm to investigate unexplored regions of the search space and simultaneously keep the best solutions found by the algorithm. The proposed approach is compared with two Differential Evolution variants on a recently proposed benchmark suite. Empirical results indicate that the proposed niching algorithm is competitive and very promising. It exhibits a robust and stable behavior, whilst the incorporation of the dynamic archive seems to tackle the population size influence effectively. Moreover, it alleviates the problem of having to fine-tune the population size parameter in a niching algorithm.},
	doi = {10.1109/CEC.2013.6557556},
	url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6557556},
	author = {M. G. Epitropakis and Xiaodong Li and Edmund K. Burke}
}
@conference {9,
	title = {Density Based Projection Pursuit Clustering},
	booktitle = {IEEE Congress on Evolutionary Computation, 2012. CEC 2012. (IEEE World Congress on Computational Intelligence)},
	year = {2012},
	month = {June},
	address = {Brisbane, Australia},
	abstract = {Clustering of high dimensional data is a very important task in Data Mining. In dealing with such data, we typically need to use methods like Principal Component Analysis and Projection Pursuit, to find interesting lower dimensional directions to project the data and hence reduce their dimensionality in a manageable size. In this work, we propose a new criterion of direction interestingness, which incorporates information from the density of the projected data. Subsequently, we utilize the Differential Evolution algorithm to perform optimization over the space of the projections and hence construct a new hierarchical clustering algorithmic scheme. The new algorithm shows promising performance over a series of real and simulated data.},
	doi = {10.1109/CEC.2012.6253006},
	author = {S. K. Tasoulis and M. G. Epitropakis and V. P. Plagianakos and D. K. Tasoulis}
}
