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In this paper, we focus on the study of evolutionary algorithms for solving multiobjective optimization problems with a large number of objectives. First, a comparative study of a newly developed dynamical multiobjective evolutionary algorithm (DMOEA) and some modern algorithms, such as the indicator-based evolutionary algorithm, multiple single objective(More)
Different cellular signal transduction pathways are often interconnected, so that the potential for undesirable crosstalk between pathways exists. Nevertheless, signaling networks have evolved that maintain specificity from signal to cellular response. Here, we develop a framework for the analysis of networks containing two or more interconnected signaling(More)
Cellular signaling pathways transduce extracellular signals into appropriate responses. These pathways are typically interconnected to form networks, often with different pathways sharing similar or identical components. A consequence of this connectedness is the potential for cross talk, some of which may be undesirable. Indeed, experimental evidence(More)
Cells sense several kinds of stimuli and trigger corresponding responses through signaling pathways. As a result, cells must process and integrate multiple signals in parallel to maintain specificity and avoid erroneous cross-talk. In this study, we focus our theoretical effort on understanding specificity of a model network system in yeast, Saccharomyces(More)
In this paper, a new dynamical evolutionary algorithm (DEA) is presented based on the theory of statistical mechanics. The novelty of this kind of dynamical evolutionary algorithm is that all individuals in a population (called particles in a dynamical system) are running and searching with their population evolving driven by a new selecting mechanism. This(More)
Although differential evolution (DE) algorithms have shown great power in solving continuous optimization problems, it is still a challenging task to design an efficient binary variant of DE algorithm. In this paper, we propose a binary learning differential evolution (BLDE) algorithm, which can efficiently search the feasible region by learning from the(More)