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Article Citation - WoS: 11Citation - Scopus: 13Choice Functions for Autonomous Search in Constraint Programming: Ga Vs. Pso(Univ Osijek, Tech Fac, 2013) Soto, Ricardo; Crawford, Broderick; Misra, Sanjay; Palma, Wenceslao; Monfroy, Eric; Castro, Carlos; Paredes, Fernando; Computer EngineeringThe variable and value ordering heuristics are a key element in Constraint Programming. Known together as the enumeration strategy they may have important consequences on the solving process. However, a suitable selection of heuristics is quite hard as their behaviour is complicated to predict. Autonomous search has been recently proposed to handle this concern. The idea is to dynamically replace strategies that exhibit poor performances by more promising ones during the solving process. This replacement is carried out by a choice function, which evaluates a given strategy in a given amount of time via quality indicators. An important phase of this process is performed by an optimizer, which aims at finely tuning the choice function in order to guarantee a precise evaluation of strategies. In this paper we evaluate the performance of two powerful choice functions: the first one supported by a genetic algorithm and the second one by a particle swarm optimizer. We present interesting results and we demonstrate the feasibility of using those optimization techniques for Autonomous Search in a Constraint Programming context.Article Citation - WoS: 2Constraint Programming for Optimal Design of Architectures for Water Distribution Tanks and Reservoirs: a Case Study(Univ Osijek, Tech Fac, 2014) Soto, Ricardo; Crawford, Broderick; Misra, Sanjay; Monfroy, Eric; Palma, Wenceslao; Castro, Carlos; Paredes, FernandoA water distribution system is an essential component of any urban infrastructure system. Its design is commonly a hard task mainly due to the presence of several complex interrelated parameters. Among others, some parameters to study are the water demand, pressure requirements, topography, location of resources, system reliability, and energy uses. In this paper, we focus on a real case of water distribution system in order to minimize installation costs by satisfying the given system requirements. We solve the problem by using state-of-the-art Constraint Programming techniques combined with Interval Analysis for rigorously handling continuous decision variables. Experimental results demonstrate the feasibility of the proposed approach, where the global optimum is reached in all instances and in reasonable runtime.Article Citation - WoS: 7Citation - Scopus: 13An Expert System for the Diagnosis of Sexually Transmitted Diseases - Esstd(Ios Press, 2017) Thompson, Temitope; Sowunmi, Olaperi; Misra, Sanjay; Fernandez-Sanz, Luis; Crawford, Broderick; Soto, RicardoOver 93 million people get ill with sexually transmitted diseases in sub-Saharan Africa. However, research has shown that people with sexually transmitted diseases find it difficult to share their problem with a physician due to societal discrimination in Africa. Due to this problem, we have implemented a medical expert system for diagnosing sexually transmitted diseases (ESSTD) that maintains the anonymity of the individuals. The patients diagnose themselves by answering questions provided by the system. This paper presents the design and development of the system. Forward chaining rules were used to implement the knowledge base and the system is easily accessible on mobile platforms. The Java Expert System Shell was used for its inference engine and the system was validated by domain experts. It is useful because it helps to maintain anonymity for patients with STD.Conference Object Citation - WoS: 6Citation - Scopus: 6Comparing Cuckoo Search, Bee Colony, Firefly Optimization, and Electromagnetism-Like Algorithms for Solving the Set Covering Problem(Springer-verlag Berlin, 2015) Soto, Ricardo; Crawford, Broderick; Galleguillos, Cristian; Barraza, Jorge; Lizama, Sebastian; Munoz, Alexis; Paredes, FernandoThe set covering problem is a classical model in the subject of combinatorial optimization for service allocation, that consists in finding a set of solutions for covering a range of needs at the lowest possible cost. In this paper, we report various approximate methods to solve this problem, such as Cuckoo Search, Bee Colony, Firefly Optimization, and Electromagnetism-Like Algorithms. We illustrate experimental results of these metaheuristics for solving a set of 65 non-unicost set covering problems from the Beasley's OR-Library.Conference Object Citation - WoS: 2Citation - Scopus: 3Autonomous Tuning for Constraint Programming Via Artificial Bee Colony Optimization(Springer-verlag Berlin, 2015) Soto, Ricardo; Crawford, Broderick; Mella, Felipe; Flores, Javier; Galleguillos, Cristian; Misra, Sanjay; Paredes, FernandoConstraint Programming allows the resolution of complex problems, mainly combinatorial ones. These problems are defined by a set of variables that are subject to a domain of possible values and a set of constraints. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. Autonomous Search provides the ability to the solver to self-tune its enumeration strategy in order to select the most appropriate one for each part of the search tree. This self-tuning process is commonly supported by an optimizer which attempts to maximize the quality of the search process, that is, to accelerate the resolution. In this work, we present a new optimizer for self-tuning in constraint programming based on artificial bee colonies. We report encouraging results where our autonomous tuning approach clearly improves the performance of the resolution process.
