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To understand the emergent behavior of biochemical systems, computational analyses generally require the inference of unknown reaction kinetic constants, a problem known as parameter estimation (PE). In this work we propose a PE methodology that exploits Particle Swarm Optimization (PSO) to examine a set of candidate kinetic parameterizations, whose fitness is evaluated by comparing given target time-series...
A gene regulatory network reveals the regulatory relationships among genes at a cellular level. The accurate reconstruction of such networks using computational tools, from time series genetic expression data, is crucial to the understanding of the proper functioning of a living organism. Investigations in this domain focused mainly on the identification of as many true regulations as possible. This...
Differential Evolution (DE) has been successfully applied to various optimization problems. The performance of DE is affected by algorithm parameters such as a scaling factor F and a crossover rate CR. Many studies have been done to control the parameters adaptively. One of the most successful studies on parameter control is JADE. In JADE, the two parameter values are generated according to two probability...
Differential evolution (DE) is a high performance and easy to implement evolutionary algorithm. The DE algorithm with small population size (i.e., micro-DE) can further increase the efficiency of the algorithm. However, it also decreases its exploration capability, causing stagnation and pre-mature convergence. In this paper, the idea of exploration enhancement at the mutation level is proposed. The...
Opposition-based learning (OBL) is a recently proposed method, which is successfully used to accelerate the search process of some well-known techniques in soft computing, such as swarm and evolutionary algorithms, artificial neural networks, reinforcement learning, and fuzzy logic systems. Among these opposition-based algorithms, opposition-based differential evolution (ODE) is one of the most popular...
Differential Evolution (DE), a population-based stochastic search technique is adept at solving real-world optimization problems. Unlike most population based algorithms, the use of DE is usually inexpedient in solving expensive optimization problems as the computational costs of these simulations are excessively high. This problem can be resolved by commingling surrogate model in DE that approximates...
This paper proposes a new evolutionary algorithm (EA), which is called the natural aggregation algorithm (NAA). NAA is inspired by the collective decision making intelligence of the group-living animals. Distinguished from other EAs, NAA distributes individuals to several sub-populations (called ‘shelters’), and uses a stochastic migration model to dynamically mitigate the individuals among the shelters...
Due to the gradient-free mechanism, flexibility, high local optima avoidance, and simplicity, meta-heuristics have been reliable alternatives to conventional optimisation techniques over the course of last two decades. This has resulted in the application of such techniques in diverse branches of science and technology. Despite all the successful applications, meta-heuristics are less effective in...
There is a growing number of studies on general purpose metaheuristics that are directly applicable to multiple domains. Parameter setting is a particular issue considering that many of such search methods come with a set of parameters to be configured. Fuzzy logic has been used extensively in control applications and is known for its ability to handle uncertainty. In this study, we investigate the...
In this paper, it is experimentally verified that TDGA (Thermo Dynamical Genetic Algorithm) is effective in solving a function optimization problem using Genetic Algorithms, because of its sustainability of population diversity and efficiency of searching for solutions. We experimentally and quantitatively verify the hypothesis that we can improve the ratio of searching for the optimum solution and...
This work presents different opposite learning strategies for Ant Knapsack, an ant based algorithm for the Multidimensional Knapsack Problem. We propose to include a previous opposite learning phase to Ant Knapsack, for discarding regions of the search space. This opposite knowledge is then used by Ant Knapsack for solving the original problem. The objective is to improve the search process of Ant...
In Job Shop Scheduling (JSS) problems, there are usually many conflicting objectives to consider, such as the makespan, mean flowtime, maximal tardiness, number of tardy jobs, etc. Most studies considered these objectives separately or aggregated them into a single objective (fitness function) and treat the problem as a single-objective optimization. Very few studies attempted to solve the multi-objective...
Resource constrained project scheduling problem (RCPSP) is a well known problem in the area of discrete optimization. It involves scheduling a given set of activities such that they are completed within minimum possible time, while satisfying a given set of precedence and resource constraints. RCPSP has a wide applicability in a number of industries, such as engineering, management, software, etc...
The classification performance of a weighted voting ensemble of classifiers largely depends on the proper weight chosen for each base classifier's vote. In this paper, we propose the use of Differential Evolution algorithm for adjustment of voting-weights of base classifiers used in a heterogeneous ensemble of classifiers (HEoC). We used the average Matthews Correlation Coefficient (MCC), calculated...
Load Patterns (LPs) clustering has a broad range of applications, such as tariff formulation, power system planning, load forecasting, and so on. In this paper, we develop a multi-objective version of Differential Evolution (DE) using a Pareto Tournament (PT) selection to solve the LP clustering problem. Our automatic DE LP clustering (ADE-LPC) algorithm provides an entire Pareto front, and by incorporating...
Performing data mining tasks on raw time series is inefficient as these data are high-dimensional by nature. Instead, time series are first pre-processed using several techniques before the different data mining tasks can be performed. In general, there are two main approaches to pre-process time series. The first is what we call landmark methods. These methods are based on finding characteristic...
Document clustering is useful for many research areas such as Text Mining and Information Retrieval. Therefore, it is desirable to be able to cluster documents accurately. The clustering quality depends not only on the clustering algorithm used but also on the way text is represented in the algorithm. Text is typically represented using the All-Words Vector Space Model in text mining applications...
Entity matching is to map the records to the corresponding entity. It is a well known problem studied by many researchers over the last few years. In bibliographic database, the data evolve over time. For example, the email id of an author in DBLP and ArnetMiner which are two bibliographic databases changes with time. Authors also keep on changing their affiliations. The set of authors with whom they...
This paper proposes a hybrid approach to optimal day-ahead pricing for demand response management. At the customer-side, a comprehensive energy management system, which includes most commonly used appliances and an effective waiting time cost model is proposed to manage the energy usages in households (lower level problem). At the retailer-side, the best retail prices are determined to maximize the...
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