作者：
Cotta, CTroya, JMUniv Malaga
ETSI Informat Dept Lenguajes & Ciencias Computac ETSI Informat E-29071 Malaga Spain
We consider the problem of inferring a genetic network from noisy data. This is done under the Temporal Boolean Network Model. Owing to the hardness of the problem, we propose an heuristic approach based on the combin...
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We consider the problem of inferring a genetic network from noisy data. This is done under the Temporal Boolean Network Model. Owing to the hardness of the problem, we propose an heuristic approach based on the combined utilization of evolutionary algorithms and other existing algorithms. The main features of this approach are the, heuristic seeding of the initial population, the utilization of a specialized recombination operator, and the use of a majority-voting procedure in order to build a consensus solution. Experimental results provide support for the potential usefulness of this approach. (C) 2003 Elsevier B.V. All rights reserved.
evolutionary algorithms (EA) have been extensively used in research to resolve optimization problems involving computationally intensive objective function evaluations. It is even more interesting to use a low-cost di...
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evolutionary algorithms (EA) have been extensively used in research to resolve optimization problems involving computationally intensive objective function evaluations. It is even more interesting to use a low-cost distributed computing platform based on Volunteer Computing (VC), to perform such optimizations. The downside is that VC compute nodes' volatility and unreliability associated with the level of task dependency introduced by parallel EA's tend to delay the algorithm's progress. This work proposes an enhanced scheduling of the BOINC (Berkeley Open Infrastructure for Network Computing) tasks associated with a Genetic Algorithm (GA) that aims at improving the performance of the algorithm. BOINC is the most popular middleware used for VC. While the GA has been chosen as it is the most commonly used EA, this approach is applicable to most of iterative EA's. The scheduling performs a matchmaking between a pool of tasks, classified according to their potential (predicted) fitness, and the pool of available hosts, classified according to their reliability. The scheduling technique have been implemented in a simulation environment and tested with benchmark functions. It proved to be effective in increasing the convergence speed and reducing the execution time of the GA. (C) 2012 Elsevier B.V. All rights reserved.
One of the major concerns in evolutionary algorithms is the premature convergence caused by what is known as the Exploration and Exploitation Balance problem. To maintain this balance, population diversity should be m...
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One of the major concerns in evolutionary algorithms is the premature convergence caused by what is known as the Exploration and Exploitation Balance problem. To maintain this balance, population diversity should be maintained during the initialization/optimization process. Maintaining diversity can be done through different strategies, but they commonly answer one question: when to introduce more diversity to the population? To answer this question there should be diversity metrics upon which a decision can be made to add diversity;consequently, add/reduce exploration/exploitation. There are as many diversity metrics as many problems and representations. That is, diversity metrics are very problem-specific. This work provides diversity metrics for the variable-length chromosome Genetic Algorithm for Shortest Path. The suggested metrics consider the varying lengths of the chromosomes, problem representation, and the search space. To measure chromosome-length diversity, a novel chromosome-length-based metric has been proposed. By exploiting the fact that the possible genes that can form any chromosome are well known in this specific direct-encoded population, a new simple metric that measures the representation of genes in the initial population is proposed and experimentally investigated. The presented metrics put through an extensive simulation and comparatively studied. Relationship between the proposed metrics has been quantified using Principal Component Analysis under varying network/population sizes.
We present an evolutionary algorithm to optimize controller parameters for a nonlinear nutator system which is implemented in twin voice coil motors (VCMs). In this paper, genetics-based algorithm is applied to tune t...
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We present an evolutionary algorithm to optimize controller parameters for a nonlinear nutator system which is implemented in twin voice coil motors (VCMs). In this paper, genetics-based algorithm is applied to tune the proportional-integral-derivative (PID) controller for the multi-input multi-output (MIMO) coupling system. The result has been validated that this algorithm can find out all six PID control parameters in optimal and robust senses. The controller architecture of the nutator system is constructed to meet the specifications of the coupled mirror and rocker parallel loops, both adopting PID control. Two main linear VCMs operating in a push-pull organization drive the nutator subreflector. The mandatory operation mode for the nutator system is two-position switching. The required technical specifications for the nutator control system are extremely precise requirements, system tracking control with a disturbance force more challenging than that in ordinary systems. Simulation results have demonstrated the superiority of the evolutionary algorithm, satisfying the Atacama large millimeter/submillimeter array (ALMA) severe conditions and requirements.
In this paper, we present a new approach for training set selection in large size data sets. The algorithm consists on the combination of stratification and evolutionary algorithms. The stratification reduces the size...
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In this paper, we present a new approach for training set selection in large size data sets. The algorithm consists on the combination of stratification and evolutionary algorithms. The stratification reduces the size of domain where the selection is applied while the evolutionary method selects the most representative instances. The performance of the proposal is compared with seven non- evolutionary algorithms, in stratified execution. The analysis follows two evaluating approaches: balance between reduction and accuracy of the subsets selected, and balance between interpretability and accuracy of the representation models associated to these subsets. The algorithms have been assessed on large and huge size data sets. The study shows that the stratified evolutionary instance selection consistently outperforms the non- evolutionary ones. The main advantages are: high instance reduction rates, high classification accuracy and models with high interpretability. (C) 2005 Elsevier B. V. All rights reserved.
Relationship-based access control (ReBAC) provides a high level of expressiveness and flexibility that promotes security and information sharing. We formulate ReBAC as an object-oriented extension of attribute-based a...
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Relationship-based access control (ReBAC) provides a high level of expressiveness and flexibility that promotes security and information sharing. We formulate ReBAC as an object-oriented extension of attribute-based access control (ABAC) in which relationships are expressed using fields that refer to other objects, and path expressions are used to follow chains of relationships between objects. ReBAC policy mining algorithms have potential to significantly reduce the cost of migration from legacy access control systems to ReBAC, by partially automating the development of a ReBAC policy from an existing access control policy and attribute data. This paper presents two algorithms for mining ReBAC policies from access control lists (ACLs) and attribute data represented as an object model: a greedy algorithm guided by heuristics, and a grammar-based evolutionary algorithm. An evaluation of the algorithms on four sample policies and two large case studies demonstrates their effectiveness. (C) 2018 Elsevier Ltd. All rights reserved.
This paper outlines the development of a new evolutionary algorithms based timetabling (EAT) tool for solving course scheduling problems that include a genetic algorithm (GA) and a memetic algorithm (MA). Reproduction...
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This paper outlines the development of a new evolutionary algorithms based timetabling (EAT) tool for solving course scheduling problems that include a genetic algorithm (GA) and a memetic algorithm (MA). Reproduction processes may generate infeasible solutions. Previous research has used repair processes that have been applied after a population of chromosomes has been generated. This research developed a new approach which (i) modified the genetic operators to prevent the creation of infeasible solutions before chromosomes were added to the population;(ii) included the clonal selection algorithm (CSA);and the elitist strategy (ES) to improve the quality of the solutions produced. This approach was adopted by both the GA and MA within the EAT. The MA was further modified to include hill climbing local search. The EAT program was tested using 14 benchmark timetabling problems from the literature using a sequential experimental design, which included a fractional factorial screening experiment. Experiments were conducted to (i) test the performance of the proposed modified algorithms;(ii) identify which factors and interactions were statistically significant;(iii) identify appropriate parameters for the GA and MA;and (iv) compare the performance of the various hybrid algorithms. The genetic algorithm with modified genetic operators produced an average improvement of over 50%.
Over the past few years, a continually increasing number of research efforts have investigated the application of evolutionary computation techniques for the solution of scheduling problems, Scheduling can pose extrem...
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Over the past few years, a continually increasing number of research efforts have investigated the application of evolutionary computation techniques for the solution of scheduling problems, Scheduling can pose extremely complex combinatorial optimization problems, which belong to the NP-hard family. Last enhancements on evolutionary algorithms include new multirecombinative approaches. Multiple Crossovers Per Couple (MCPC) allows multiple crossovers on the couple selected for mating and Multiple Crossovers on Multiple Parents (MCMP) do this but on a set of more than two parents. Techniques for preventing incest also help to avoid premature convergence. Issues on representation and operators influence efficiency and efficacy of the algorithm. The present paper shows how enhanced evolutionary approaches, can solve the Job Shop Scheduling Problem (JSSP) in single and multiobjective optimization. (C) 2002 Elsevier Science B.V. All rights reserved.
Good knowledge would be expected to help a knowledge-based algorithm more than bad knowledge. In this research, the precise effect of good versus bad knowledge on evolutionary algorithms is explored. The testable hypo...
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Good knowledge would be expected to help a knowledge-based algorithm more than bad knowledge. In this research, the precise effect of good versus bad knowledge on evolutionary algorithms is explored. The testable hypothesis of this paper is that good knowledge will have a significant effect on the evolutionary mutation process, whereas bad knowledge will have no significant effect. A knowledge-guided evolutionary algorithm is developed where ontologies, representing knowledge, are applied to the mutation process. Bad knowledge is represented as a randomly generated ontology, while good knowledge is represented by ontologies constructed with domain knowledge and following a formal ontology development process. Decision trees are evolved to solve a classification problem. Fitness is classification accuracy. The experiment is replicated over 2 data-sets from different domains with one being time-series, financial data and the other being wine data. As hypothesized, poorly constructed, or bad knowledge, has no effect while good knowledge is shown to have a significant effect. Bad knowledge, being random in character in these experiments, has understandably no impact on an already random mutation process. However, employing knowledge to guide the mutation process significantly constrains the traversal of the search space. Employing knowledge in an evolutionary algorithm has the potential to increase the efficiency and accuracy of evolutionary algorithms. (C) 2015 Elsevier Ltd. All rights reserved.
evolutionary algorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution. Recent theoretical studies have shown that EAs can achieve good approximation guarantees for solving the proble...
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evolutionary algorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution. Recent theoretical studies have shown that EAs can achieve good approximation guarantees for solving the problem classes of submodular optimization, which have a wide range of applications, such as maximum coverage, sparse regression, influence maximization, document summarization and sensor placement, just to name a few. Though they have provided some theoretical explanation for the general-purpose nature of EAs, the considered submodular objective functions are defined only over sets or multisets. To complement this line of research, this paper studies the problem class of maximizing monotone submodular functions over sequences, where the objective function depends on the order of items. We prove that for each kind of previously studied monotone submodular objective functions over sequences, i.e., prefix monotone submodular functions, weakly monotone and strongly submodular functions, and DAG monotone submodular functions, a simple multi-objective EA, i.e., GSEMO, can always reach or improve the best known approximation guarantee after running polynomial time in expectation. Note that these best-known approximation guarantees can be obtained only by different greedy-style algorithms before. Empirical studies on various applications, e.g., accomplishing tasks, maximizing information gain, search-and-tracking and recommender systems, show the excellent performance of the GSEMO.(c) 2022 Elsevier B.V. All rights reserved.
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