Browsing by Author "Ong, Yew-Soon"

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    Editorial to special issue on evolutionary computation in dynamic and uncertain environments
    (Springer, 2006-12) Ong, Yew-Soon; Jin, Yaochu; Yang, Shengxiang
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    Evolutionary Computation in Dynamic and Uncertain Environments
    (Springer, 2007-03) Yang, Shengxiang; Ong, Yew-Soon; Jin, Yaochu
    This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The motivation for this book arises from the fact that some degree of uncertainty in characterizing any realistic engineering systems is inevitable. Representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums, are presented. "Evolutionary Computation in Dynamic and Uncertain Environments" is a valuable reference for scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, natural computing and evolutionary computation.
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    Handling constrained many-objective optimization problems via problem transformation
    (IEEE Press, 2020-11-18) Jiao, Ruwang; Zeng, Sanyou; Li, Changhe; Yang, Shengxiang; Ong, Yew-Soon
    Objectives optimization and constraints satisfaction are two equally important goals to solve constrained many-objective optimization problems (CMaOPs). However, most existing studies for CMaOPs can be classified as feasibility-driven constrained many-objective evolutionary algorithms (C-MaOEAs), they always give priority to satisfy constraints, while ignoring the maintenance of the population diversity for dealing with conflicting objectives. Consequently, the population may be pushed towards some locally feasible optimal or locally infeasible areas in the high-dimensional objective space. To alleviate this issue, this paper presents a problem transformation technique, which transforms a CMaOP into a dynamic CMaOP (DCMaOP) for handling constraints and optimizing objectives simultaneously, to help the population cross the large and discrete infeasible regions. The well-known reference-point-based NSGA-III is tailored under the problem transformation model to solve CMaOPs, namely DCNSGA-III. In this paper, ε -feasible solutions play an important role in the proposed algorithm. To this end, in DCNSGA-III, a mating selection mechanism and an environmental selection operator are designed to generate and choose high-quality ε-feasible offspring solutions, respectively. The proposed algorithm is evaluated on a series of benchmark CMaOPs with 3, 5, 8, 10, and 15 objectives and compared against six state-of-the-art CMaOEAs. The experimental results indicate that the proposed algorithm is highly competitive for solving CMaOPs.
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    Ockham's Razor in memetic computing: Three stage optimal memetic exploration
    (Elsevier, 2012) Iacca, Giovanni; Neri, Ferrante; Mininno, Ernesto; Ong, Yew-Soon; Lim, Meng-Hiot
    Memetic computing is a subject in computer science which considers complex structures as the combination of simple agents, memes, whose evolutionary interactions lead to intelligent structures capable of problem-solving. This paper focuses on memetic computing optimization algorithms and proposes a counter-tendency approach for algorithmic design. Research in the field tends to go in the direction of improving existing algorithms by combining different methods or through the formulation of more complicated structures. Contrary to this trend, we instead focus on simplicity, proposing a structurally simple algorithm with emphasis on processing only one solution at a time. The proposed algorithm, namely three stage optimal memetic exploration, is composed of three memes; the first stochastic and with a long search radius, the second stochastic and with a moderate search radius and the third deterministic and with a short search radius. The bottom-up combination of the three operators by means of a natural trial and error logic, generates a robust and efficient optimizer, capable of competing with modern complex and computationally expensive algorithms. This is suggestive of the fact that complexity in algorithmic structures can be unnecessary, if not detrimental, and that simple bottom-up approaches are likely to be competitive is here invoked as an extension to memetic computing basing on the philosophical concept of Ockham’s Razor. An extensive experimental setup on various test problems and one digital signal processing application is presented. Numerical results show that the proposed approach, despite its simplicity and low computational cost displays a very good performance on several problems, and is competitive with sophisticated algorithms representing the-state-of-the-art in computational intelligence optimization.