Past decades have seen the rapid development of microarray technologies making available large amounts of gene expression data. Hence, it has become increasingly important to have reliable methods to interpret this in...
详细信息
Past decades have seen the rapid development of microarray technologies making available large amounts of gene expression data. Hence, it has become increasingly important to have reliable methods to interpret this information in order to discover new biological knowledge. In this review paper we aim to describe the main existing evolutionary methods that analyze microarray gene expression data by means of biclustering techniques. Strategies will be classified according to the evaluation metric used to quantify the quality of the biclusters. In this context, the main evaluation measures, namely mean squared residue, virtual error and transposed virtual error, are first presented. Then, the main evolutionary algorithms, which find biclusters in gene expression data matrices using those metrics, are described and compared.
Multi-modality can cause serious problems for many optimisers, often resulting convergence to sub-optimal modes. Even when this is not the case, it is often useful to locate and memorise a range of modes in the design...
详细信息
Multi-modality can cause serious problems for many optimisers, often resulting convergence to sub-optimal modes. Even when this is not the case, it is often useful to locate and memorise a range of modes in the design space. This is because "optimal" decision parameter combinations may not actually be feasible when moving from a mathematical model emulating the real problem, to engineering an actual solution, making a range of disparate modal solutions of practical use. This paper builds upon our work on the use of a collection of localised search algorithms for niche/mode discovery which we presented at UKCI 2013 when using a collection of surrogate models to guide mode search. Here we present the results of using a collection of exploitative local evolutionary algorithms (EAs) within the same general framework. The algorithm dynamically adjusts its population size according to the number of regions it encounters that it believes contain a mode and uses localised EAs to guide the mode exploitation. We find that using a collection of localised EAs, which have limited communication with each other, produces competitive results with the current state-of-the-art multi-modal optimisation approaches on the CEC 2013 benchmark functions.
This article presents an empirical study devoted to characterize the computational efficiency behavior of an evolutionary algorithm (usually called canonical) as a C program. The study analyzes the effects of several ...
详细信息
This article presents an empirical study devoted to characterize the computational efficiency behavior of an evolutionary algorithm (usually called canonical) as a C program. The study analyzes the effects of several implementation decisions on the execution time of the resulting evolutionary algorithm. The implementation decisions studied include: memory utilization (using dynamic vs. static variables and local vs. global variables), methods for ordering the population, code substitution mechanisms, and the routines for generating pseudorandom numbers within the evolutionary algorithm. The results obtained in the experimental analysis allow us to conclude that significant improvements in efficiency can be gained by applying simple guidelines to best program an evolutionary algorithm in C. Copyright (C) 2013 John Wiley & Sons, Ltd.
Inspired on decision trees and evolutionary algorithms, this paper proposes a learning algorithm of constructive neural networks that relies on three principles: to layout the neurons in a tree-like structure;to train...
详细信息
ISBN:
(纸本)9781509006212
Inspired on decision trees and evolutionary algorithms, this paper proposes a learning algorithm of constructive neural networks that relies on three principles: to layout the neurons in a tree-like structure;to train each neuron individually;and, to optimize all the weights using an evolutionary approach. This way, it is expected to advance in two main questions concerning multilayer perceptrons (MLPs): how to determine the network architecture and how to build models that are more comprehensible. Based on the normalized information gain of each attribute, the algorithm builds the network architecture. In the process, it automatically creates a set of training examples for each individual neuron and executes single-cell learning. Once the network is created and trained, particle swarm optimization is utilized to evolve the connections of the network. Five metrics were utilized to validate the method when compared to decision trees and MLPs: accuracy, sensitivity, specificity, precision and comprehensibility. The experiments were executed in thirteen different databases and the results suggest that the proposed algorithm can generate neural networks with good classification performance and more comprehensible.
The inverse modeling of heat transfer is a useful tool in analyzing contact heat transfer at the ingot surfaces during the continuous casting process. The determination of the boundary conditions involves an experimen...
详细信息
The inverse modeling of heat transfer is a useful tool in analyzing contact heat transfer at the ingot surfaces during the continuous casting process. The determination of the boundary conditions involves an experimental work consisting in the evaluation of the thermal history, generally at the casting surface, experimentally provided by infrared pyrometers. Additionally, numerical simulations, based on the solution of the 2D transient heat conduction equation, are performed in order to be inversely solved in response to the measured thermal data furnished by the sensor. Due to computational time consumption during simulations in searching cooling conditions, this work proposes an interaction between natural inspired algorithms, called evolutionary algorithms, and the numerical model in order to speed up the searching process. The present work aims to compare three algorithms, namely genetic algorithm, improved stochastic ranking evolutionary strategy, and evolutionary strategy with Cauchy distribution. The latter develops a metaheuristic version of an evolutionary strategy workflow, using a Cauchy random number function to generate each individual, instead of the usual uniform distribution function available in almost all programming languages. The surface temperature, solid shell, and molten pool profiles from the determined cooling conditions are analyzed in terms of casting quality.
Decomposition based algorithms perform well when a suitable set of weights are provided;however determining a good set of weights a priori for real-world problems is usually not straightforward due to a lack of knowle...
详细信息
Decomposition based algorithms perform well when a suitable set of weights are provided;however determining a good set of weights a priori for real-world problems is usually not straightforward due to a lack of knowledge about the geometry of the problem. This study proposes a novel algorithm called preference-inspired co-evolutionary algorithm using weights (PICEA-w) in which weights are co-evolved with candidate solutions during the search process. The co-evolution enables suitable weights to be constructed adaptively during the optimisation process, thus guiding candidate solutions towards the Pareto optimal front effectively. The benefits of co-evolution are demonstrated by comparing PICEA-w against other leading decomposition based algorithms that use random, evenly distributed and adaptive weights on a set of problems encompassing the range of problem geometries likely to be seen in practice, including simultaneous optimisation of up to seven conflicting objectives. Experimental results show that PICEA-w outperforms the comparison algorithms for most of the problems and is less sensitive to the problem geometry. (C) 2014 Elsevier B.V. All rights reserved.
Two evolutionary algorithms (EAs) are assessed in this paper to design optimal operating rules (ORs) for Water Resource Systems (WRS). The assessment is established through a parameter analysis of both algorithms in a...
详细信息
Two evolutionary algorithms (EAs) are assessed in this paper to design optimal operating rules (ORs) for Water Resource Systems (WRS). The assessment is established through a parameter analysis of both algorithms in a theoretical case, and the methodology described in this paper is applied to a complex, real case. These two applications allow us to analyse an algorithm's properties and performance by defining ORs, how an algorithm's termination/convergence criteria affect the results and the importance of decision-makers participating in the optimisation process. The former analysis reflects the need for correctly defining the important algorithm parameters to ensure an optimal result and how the greater number of termination conditions makes the algorithm an efficient tool for obtaining optimal ORs in less time. Finally, in the complex real case application, we discuss the participation value of decision-makers toward correctly defining the objectives and making decisions in the post-process. (C) 2014 Elsevier Ltd. All rights reserved.
In this paper, neuro-fuzzy based group method of data handling (NF-GMDH) as an adaptive learning network was utilized to predict the local scour depth at pile groups under clear-water conditions. The NF-GMDH network w...
详细信息
In this paper, neuro-fuzzy based group method of data handling (NF-GMDH) as an adaptive learning network was utilized to predict the local scour depth at pile groups under clear-water conditions. The NF-GMDH network was developed using particle swarm optimization (PSO) and gravitational search algorithm (GSA). Effective parameters on the scour depth include bed sediment size, geometric properties, piles spacing, arrangements of pile group, and flow characteristics in upstream of group piles and critical flow condition due to initiation of particles' motion on bed surface. Nine dimensional parameters were considered to define a functional relationship between input and output variables. The NF-GMDH models were carried out using datasets collected from the literature. The efficiency of training stages for both NF-GMDH-PSO and NF-GMDH-GSA models was investigated. Testing results for the NF-GMDH networks were compared with the empirical equations. The NF-GMDH-PSO network produced more efficient performance (R=0.95 and RMSE=0.035) for scour depth prediction compared with the NF-GMDH-GSA model (R=0.94 and RMSE=0.036). The NF-GMDH models indicated quite higher accuracy of scour prediction, compared with the empirical equations (R=0.44 and RMSE = 0.127). Also, the sensitivity analysis indicated that pier diameter was the most significant parameter on scour depth. (C) 2015 Elsevier Ltd. All rights reserved.
evolutionary algorithms (EAs) excel in optimizing systems with a large number of variables. Previous mathematical and empirical studies have shown that opposition-based algorithms can improve EA performance. We review...
详细信息
evolutionary algorithms (EAs) excel in optimizing systems with a large number of variables. Previous mathematical and empirical studies have shown that opposition-based algorithms can improve EA performance. We review existing opposition-based algorithms and introduce a new one. The proposed algorithm is named fitness-based quasi-reflection and employs the relative fitness of solution candidates to generate new individuals. We provide the probabilistic analysis to prove that among all the opposition-based methods that we investigate, fitness-based quasi-reflection has the highest probability of being closer to the solution of an optimization problem. We support our theoretical findings via Monte Carlo simulations and discuss the use of different reflection weights. We also demonstrate the benefits of fitness-based quasi-reflection on three state-of-the-art EAs that have competed at IEEE CEC competitions. The experimental results illustrate that fitness-based quasi-reflection enhances EA performance, particularly on problems with more challenging solution spaces. We found that competitive DE (CDE) which was ranked tenth in CEC 2013 competition benefited the most from opposition. CDE with fitness-based quasi-reflection improved on 21 out of the 28 problems in the CEC 2013 test suite and achieved 100% success rate on seven more problems than CDE. (C) 2015 Elsevier Ltd. All rights reserved.
In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimiz...
详细信息
In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimization problems. We introduce new high level reactive search strategies based on a generic algorithm's controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search. Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm. (C) 2015 Elsevier B.V. All rights reserved.
暂无评论