Marco Aurelio Sotelo-Figueroa

Evolving Bin Packing Heuristic Using Micro-Differential Evolution with Indirect Representation

The development of low-level heuristics for solving instances of a problem is related to the knowledge of an expert. He needs to analyze several components from the problem instance and to think out an specialized heuristic for solving the instance. However if any inherent component to the instance gets changes, then the designed heuristic may not work as it used to do it. In this paper it is presented a novel approach to generated low-level heuristics; the proposed approach implements micro-Differential Evolution for evolving an indirect representation of the Bin Packing Problem. It was used the Hard28 instance, which is a well-known and referenced Bin Packing Problem instance. The heuristics obtained by the proposed approach were compared against the well know First-Fit heuristic, the results of packing that were gotten for each heuristic were analized by the statistic non-parametric test known as Wilcoxon Signed Rank test.

Quadrupedal Robot Locomotion

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE). The system has been implemented and validated on two robot platforms; firstly, we tested our system on a quadruped robot and, secondly, on a hexapod one. In this last one, we simulated the case where two legs of the hexapod were amputated and its locomotion mechanism has been changed. For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. In the hexapod robot, we used Spartan 6 FPGA board with only 3% of resource usage. Numerical results show the effectiveness of the proposed system in both cases.

Application of the Bee Swarm Optimization BSO to the Knapsack Problem

Swarm Intelligence is the part of Artificial Intelligence based on study of actions of individuals in various decentralized systems. The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel hybrid algorithm based in Bees Algorithm and Particle Swarm Optimization is applied to the Knapsack Problem. The Bee Algorithm is a new population-based search algorithm inspired by the natural foraging behavior of honey bees, it performs a kind of exploitative neighborhood search combined with random explorative search to scan the solution, but the results obtained with this algorithm in the Knapsack Problem are not very good. Although the combination of BA and PSO is given by BSO, Bee Swarm Optimization, this algorithm uses the velocity vector and the collective memories of PSO and the search based on the BA and the results are much better.

Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution.

This paper presents a method to design Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies on legged robots. It is validated through embedding its designs into a Field-Programmable Gate Array (FPGA) and implemented on a real hexapod robot. The SCPGs are automatically designed by means of a Christiansen Grammar Evolution (CGE)-based methodology. The CGE performs a solution for the configuration (synaptic weights and connections) for each neuron in the SCPG. This is carried out through the indirect representation of candidate solutions that evolve to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike trains and the SPIKE distance to lead the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the SPIKE distance-based fitness function, such as looking for Spiking Neural Networks (SNNs) with minimal connectivity or a Central Pattern Generator (CPG) able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 Degrees Of Freedom (DOFs) hexapod robot is presented.

Comparison of Metaheuristic Algorithms with a Methodology of Design for the Evaluation of Hard Constraints over the Course Timetabling Problem

The Course Timetabling problem is one of the most difficult and common problems inside an university. The main objective of this problem is to obtain a timetabling with the minimum student conflicts between assigned activities. A Methodology of design is a strategy applied before the execution of an algorithm for timetabling problem. This strategy has recently emerged, and aims to improve the obtained results as well as provide a context-independent layer to different versions of the timetabling problem. This methodology offers to an interested researcher the advantage of solving different set instances with a single algorithm which is a new paradigm in the timetabling problem state of art. In this paper the proposed methodology is tested with several metaheuristic algorithms over some well-know set instances such as Patat 2002 and 2007. The main objective in this work is to find which metaheuristic algorithm shows a better performance in terms of quality, used together with the Design Methodology. The algorithms chosen are from the area of evolutionary computation, Cellular algorithms and Swarm Intelligence. Finally our experiments use some non-parametric statistical test like Kruskal-Wallis test and wilcoxon signed rank test.

Comparison of PSO and DE for Training Neural Networks

The use of computational resources required for Feed-Forward Artificial Neural Network (FFANN) training phase by means of classical techniques such as the back propagation learning rule can be prohibitive in some applications. A good training phase is needed for a high performance of a neural network. In searching for alternative methods for training phase of FFANN, some metaheuristic techniques have been used to do this task. This paper compares the performance of Particle Swarm Optimization (PSO) and Differential Evolution (DE) as training methods for FFANN under several well-known pattern recognition instances.

Evolving and reusing Bin Packing heuristic through Grammatical Differential Evolution

The Bin Packing Problem is a classic optimization problem, over the years many heuristics have been developed to obtain better results. There are many approaches to generating heuristics automatically, those approaches are based Genetic Programming, but the heuristics generated sometimes can not be applied to the problem. Recently in the Artificial Intelligence field, the Grammar Evolution approach emerged, which generated expressions like the generated by Genetic Programming; these algorithms evolve into a grammar based on the Backus Naur Form. In the present work we show a Grammar Evolution based on Differential Evolution, which automatically generated heuristics for the Bin Packing Problem instances. Those heuristics generated by the Grammar Evolution are like the Best-Fit heuristic which was designed by humans. The works goal is to prove that is feasible to use the Grammar Evolution to automatically generate and reusing heuristics which have at least the same performance than the best generated by humans, we also propose a Grammar to improve the results obtained for a Grammar based on Genetic Programming.

Comparing Metaheuristic Algorithms on the Training Process of Spiking Neural Networks

Spiking Neural Networks are considered as the third generation of Artificial Neural Networks. In these networks, spiking neurons receive/send the information by timing of events (spikes) instead by the spike rate; as their predecessors do. Spikeprop algorithm, based on gradient descent, was developed as learning rule for training SNNs to solve pattern recognition problems; however this algorithm trends to be trapped in local minima and has several limitations. For dealing with the supervised learning on Spiking Neural Networks without the drawbacks of Spikeprop, several metaheuristics such as: Evolutionary Strategy, Particle Swarm Optimization, have been used to tune the neural parameters. This work compares the performance and the impact of some metaheuristics used for training spiking neural networks.

Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence

In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI). Particle Swarm Optimisation (PSO) is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO) is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP); it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO.

Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization

In the recent years, Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP). GE can use a diversity of search strategies including Swarm Intelligence (SI). Bee Swarm Optimization (BSO) is part of SI and it tries to solve the main problems of the Particle Swarm Optimization (PSO): the premature convergence and the poor diversity. In this paper we propose using BSO as part of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP). A comparison between BSO, PSO, and BPP heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is to propose a way to implement different algorithms as search strategy in GE. In this paper, it is proposed that the BSO obtains better results than the ones obtained by PSO, also there is a grammar proposed to generate online and offline heuristics to improve the heuristics generated by other grammars and humans.