Parameter tunning for PBIL algorithm in geometric constraint solving systems

In previous works we have shown that applying genetic algorithms to solve the Root Identification Problem is feasible and effective. The behavior of evolutive algorithms is characterized by a set of parameters that have an effect on the algorithms’ performance. In this paper we report on an empirical statistical study conducted to establish the influence of the driving parameters in the Population Based Incremental Learning (PBIL) algorithm when applied to solve the Root Identification Problem. We also identify ranges for the parameters values that optimize the algorithm performance.Postprint (author’s final draft

Parameter tuning for PBIL and CHC algorithms to solve the root identification problem in geometric constraint solving

Evolutive algorithms are among the most successful approaches for solving a number of problems where systematic search in huge domains must be performed. One problem of practical interest that falls into this category is known as emph{The Root Identification Problem} in Geometric Constraint Solving, where one solution to the geometric problem must be selected among a number of possible solutions bounded by an exponential number. In previous works we have shown that applying genetic algorithms, a category of evolutive algorithms, to solve the Root Identification Problem is both feasible and effective. The behavior of evolutive algorithms is characterized by a set of parameters that have an effect on the algorithms' performance. In this paper we report on an empirical statistical study conducted to establish the influence of the driving parameters in the PBIL and CHC evolutive algorithms when applied to solve the Root Identification Problem. We also identify ranges for the parameter values that optimize the algorithms performance.Postprint (published version

Learning to Generate Genotypes with Neural Networks

Neural networks and evolutionary computation have a rich intertwined history. They most commonly appear together when an evolutionary algorithm optimises the parameters and topology of a neural network for reinforcement learning problems, or when a neural network is applied as a surrogate fitness function to aid the evolutionary optimisation of expensive fitness functions. In this paper we take a different approach, asking the question of whether a neural network can be used to provide a mutation distribution for an evolutionary algorithm, and what advantages this approach may offer? Two modern neural network models are investigated, a Denoising Autoencoder modified to produce stochastic outputs and the Neural Autoregressive Distribution Estimator. Results show that the neural network approach to learning genotypes is able to solve many difficult discrete problems, such as MaxSat and HIFF, and regularly outperforms other evolutionary techniques

Ajustes de un algoritmo híbrido basado en SA aplicado al diseño óptimo de redes de sensores

Sensor network design problem (SNDP) in process plants includes the determination of which process variables should be measured to achieve a required degree of knowledge about the plant. We propose to solve the SNDP problem in plants of increasing size and complexity using a hybrid algorithm based on Simulated Annealing (HSA) as main metaheuristic and Tabu Search embedded with Strategic Oscillation (SOTS) as a subordinate metaheuristic. We studied the tuning of control parameters in order to improve the HSA performance. Experimental results indicate that a high-quality solution in reasonable computational times can be found by HSA effectively. Moreover, HSA shows good features solving SNDP compared with proposals from the literature.Facultad de Informátic

Ajustes de un algoritmo híbrido basado en SA aplicado al diseño óptimo de redes de sensores

Sensor network design problem (SNDP) in process plants includes the determination of which process variables should be measured to achieve a required degree of knowledge about the plant. We propose to solve the SNDP problem in plants of increasing size and complexity using a hybrid algorithm based on Simulated Annealing (HSA) as main metaheuristic and Tabu Search embedded with Strategic Oscillation (SOTS) as a subordinate metaheuristic. We studied the tuning of control parameters in order to improve the HSA performance. Experimental results indicate that a high-quality solution in reasonable computational times can be found by HSA effectively. Moreover, HSA shows good features solving SNDP compared with proposals from the literature.Facultad de Informátic

Ajustes de un algoritmo híbrido basado en SA aplicado al diseño óptimo de redes de sensores

Sensor network design problem (SNDP) in process plants includes the determination of which process variables should be measured to achieve a required degree of knowledge about the plant. We propose to solve the SNDP problem in plants of increasing size and complexity using a hybrid algorithm based on Simulated Annealing (HSA) as main metaheuristic and Tabu Search embedded with Strategic Oscillation (SOTS) as a subordinate metaheuristic. We studied the tuning of control parameters in order to improve the HSA performance. Experimental results indicate that a high-quality solution in reasonable computational times can be found by HSA effectively. Moreover, HSA shows good features solving SNDP compared with proposals from the literature.Facultad de Informátic

Tuning a hybrid SA based algorithm applied to Optimal Sensor Network Design

El problema de diseño de una red de sensores en plantas de proceso (Sensor Network Design Problem, SNDP) consiste en determinar las variables de proceso que deben ser medidas, a fin de alcanzar el grado de conocimiento requerido de dicha planta. Proponemos resolver el problema SNDP en plantas de tamaño y complejidad creciente utilizando un algoritmo híbrido basado en Recocido Simulado (Hybrid Simulated Annealing, HSA) como metaheurística principal y Búsqueda Tabú con Oscilación Estratégica como metaheurística subordinada. Investigamos los ajustes de los parámetros de control para obtener el mejor desempeño del HSA. Los resultados experimentales indican que el HSA puede efectivamente encontrar una solución de buena calidad en tiempos de computo razonable. Mas a ´ un, HSA muestra buenas ´ características en la solución de SNDP en comparación con algoritmos propuestos en la literatura.Sensor network design problem (SNDP) in process plants includes the determination of which process variables should be measured to achieve a required degree of knowledge about the plant. We propose to solve the SNDP problem in plants of increasing size and complexity using a hybrid algorithm based on Simulated Annealing (HSA) as main metaheuristic and Tabu Search embedded with Strategic Oscillation (SOTS) as a subordinate metaheuristic. We studied the tuning of control parameters in order to improve the HSA performance. Experimental results indicate that a high-quality solution in reasonable computational times can be found by HSA effectively. Moreover, HSA shows good features solving SNDP compared with proposals from the literature.Fil: Hernandez, Jose Luis. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; ArgentinaFil: Salto, Carolina. Universidad Nacional de la Pampa. Facultad de Ingeniería. Departamento de Informatica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; ArgentinaFil: Minetti, Gabriela Fabiana. Universidad Nacional de la Pampa. Facultad de Ingeniería. Departamento de Informatica; ArgentinaFil: Carnero, Mercedes del Carmen. Universidad Nacional de Río Cuarto. Facultad de Ingeniería; ArgentinaFil: Bermudez, Carlos Alberto. Universidad Nacional de la Pampa. Facultad de Ingeniería. Departamento de Informatica; ArgentinaFil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentin

Harmony Search Method: Theory and Applications

The Harmony Search (HS) method is an emerging metaheuristic optimization algorithm, which has been employed to cope with numerous challenging tasks during the past decade. In this paper, the essential theory and applications of the HS algorithm are first described and reviewed. Several typical variants of the original HS are next briefly explained. As an example of case study, a modified HS method inspired by the idea of Pareto-dominance-based ranking is also presented. It is further applied to handle a practical wind generator optimal design problem

Optimal Communication Structures for Concurrent Computing

This research focuses on communicative solvers that run concurrently and exchange information to improve performance. This “team of solvers” enables individual algorithms to communicate information regarding their progress and intermediate solutions, and allows them to synchronize memory structures with more “successful” counterparts. The result is that fewer nodes spend computational resources on “struggling” processes. The research is focused on optimization of communication structures that maximize algorithmic efficiency using the theoretical framework of Markov chains. Existing research addressing communication between the cooperative solvers on parallel systems lacks generality: Most studies consider a limited number of communication topologies and strategies, while the evaluation of different configurations is mostly limited to empirical testing. Currently, there is no theoretical framework for tuning communication between cooperative solvers to match the underlying hardware and software. Our goal is to provide such functionality by mapping solvers’ dynamics to Markov processes, and formulating the automatic tuning of communication as a well-defined optimization problem with an objective to maximize solvers’ performance metrics