Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefo...
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Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge network and the necessity of multi-objectiveoptimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in silico numerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A-B. (C) 2014 Elsevier B.V. All rights reserved.
The vehicle routing problem has attracted a lot of interest during many decades because of its wide range of applications in real life problems. This paper aims to test the efficiency and capability of bee colony opti...
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ISBN:
(纸本)9789897584435
The vehicle routing problem has attracted a lot of interest during many decades because of its wide range of applications in real life problems. This paper aims to test the efficiency and capability of bee colony optimization for this kind of problem. We present a Bee-route algorithm: a multi-objective artificial Bee Colony algorithm for the Vehicle Routing Problem with Time Windows. We have performed our experiments on well known benchmarks in the literature to compare our proposed algorithm results with other state-of-the-art algorithms.
The travelling salesperson problem is an NP-hard combinatorialoptimization problem. In this paper, we consider the multi-objective travelling salesperson problem (MTSP), both static and dynamic, with conflicting obje...
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ISBN:
(数字)9781665467087
ISBN:
(纸本)9781665467087
The travelling salesperson problem is an NP-hard combinatorialoptimization problem. In this paper, we consider the multi-objective travelling salesperson problem (MTSP), both static and dynamic, with conflicting objectives. NSGA-II and MOEA/D, two popular evolutionary multi-objectiveoptimization algorithms suffer from loss of diversity and poor convergence when applied separately on MTSP. However, both these techniques have their individual strengths. NSGA-II maintains diversity through non-dominated sorting and crowding distance selection. MOEA/D is good at exploring extreme points on the Pareto front with faster convergence. In this paper, we adopt the bicriterion framework that exploits the strengths of Pareto-Criterion (PC) and Non-Pareto Criterion (NPC) evolutionary populations. In this research, NSGA-II (PC) and MOEA/D (NPC) coevolve to compensate the diversity of each other. We further improve the convergence using local search and a hybrid of order crossover and inver-over operators. To our knowledge, this is the first work that combines NSGA-II and MOEA/D in a bicriterion framework for solving MTSP, both static and dynamic. We perform various experiments on different MTSP benchmark datasets with and without traffic factors to study static and dynamic MTSP. Our proposed algorithm is compared against standard algorithms such as NSGA-II & III, MOEA/D, and a baseline divide and conquer coevolution technique using performance metrics such as inverted generational distance, hypervolume, and the spacing metric to concurrently quantify the convergence and diversity of our proposed algorithm. We also compare our results to datasets used in the literature and show that our proposed algorithm performs empirically better than compared algorithms.
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