This paper demonstrates a new time-delayed mass action model which applies a set of delay differential equations (DDEs) to represent the dynamics of gene regulatory networks (GRNs). The mass action model is a classica...
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This paper demonstrates a new time-delayed mass action model which applies a set of delay differential equations (DDEs) to represent the dynamics of gene regulatory networks (GRNs). The mass action model is a classical model which is often used to describe the kinetics of biochemical processes, so it is fit for GRN modeling. The ability to incorporate time-delayed parameters in this model enables different time delays of interaction between genes. Moreover, an efficient learning method which employs population-basedincrementallearning (PBIL) algorithm and trigonometric differential evolution (TDE) algorithm TDE is proposed to automatically evolve the structure of the network and infer the optimal parameters from observed time-series gene expression data. Experiments on three well-known motifs of GRN and a real budding yeast cell cycle network show that the proposal can not only successfully infer the network structure and parameters but also has a strong anti-noise ability. Compared with other works, this method also has a great improvement in performances.
The Uncapacitated Location-Allocation problem (ULAP) is a major optimisation problem concerning the determination of the optimal location of facilities and the allocation of demand to them. In this paper, we present t...
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The Uncapacitated Location-Allocation problem (ULAP) is a major optimisation problem concerning the determination of the optimal location of facilities and the allocation of demand to them. In this paper, we present two novel problem variants of Non-Linear ULAP motivated by a real-world problem from the telecommunication industry: Uncapacitated Location-Allocation Resilience problem (ULARP) and Uncapacitated Location-Allocation Resilience problem with Restrictions (ULARPR). Problem sizes ranging from 16 to 100 facilities by 50 to 10000 demand points are considered. To solve the problems, we explore the components and configurations of four Genetic algorithms [1], [2], [3] and [4] selected from the ULAP literature. We aim to understand the contribution each choice makes to the GA performance and so hope to design an Optimal GA configuration for the novel problems. We also conduct comparative experiments with population-basedincrementallearning (PBIL) algorithm on ULAP. We show the effectiveness of PBIL and GA with parameter set: random and heuristic initialisation, tournament and fined_grained tournament selection, uniform crossover and bitflip mutation in solving the proposed problems.
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