Evolutionary algorithms

Misyurov D.A. Dialectical formulas based on the binary notation as the development formulas // Credo New. 2012. №2

by Dmitry Misyurov

The article suggests dialectical formulas based on the binary notation as the development formulas: formula with... more The article suggests dialectical formulas based on the binary notation as the development formulas: formula with dominant and the non-dominant elements; universal formula; formula with symbolic weight of elements; tautological formula. For example, it suggests an opportunity to use the dialectical formulas for modeling and artificial intelligence creation, etc.

An analysis of the operation of differential evolution at high and low crossover rates (2010)

by James Montgomery

IEEE CEC 2010
http://dx.doi.org/10.1109/CEC.2010.5586128

Crossover and the different faces of differential evolution searches (2010)

by James Montgomery

IEEE CEC 2010
http://dx.doi.org/10.1109/CEC.2010.5586184

Parallel Constraint Handling in a Multiobjective Evolutionary Algorithm for the Automotive Deployment Problem (2010)

by James Montgomery

IEEE e-Science Workshops
http://dx.doi.org/10.1109/eScienceW.2010.26

An Eco-inspired Evolutionary Algorithm Applied to Numerical Optimization

by Rafael Stubs Parpinelli

Ant Colony Optimization and Hypergraph Covering Problems

by Ashish Ranjan Hota

Pre-print

An Efficient Hierarchical Parallel Genetic Algorithm for Graph Coloring Problem

by Malek Mouhoub

! NOMINATED FOR BEST PAPER AWARD AT GECCO 2011 !

R. Abbasian and M. Mouhoub. An efficient hierarchical parallel genetic algorithm for graph coloring problem, 13th Annual Genetic and Evolutionary Computation Conference (GECCO 2011), ACM, pages 521-528, Dublin, Ireland, July 12-16, 2011. Also presented at the International Joint Conferences on Artificial Intelligence (IJCAI 2011), RCRA, July 2011.

Graph coloring problems (GCPs) are constraint optimization problems with various applications including scheduling,... more Graph coloring problems (GCPs) are constraint optimization problems with various applications including scheduling, time tabling, and frequency allocation. The GCP consists in finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. We propose a parallel approach based on Hierarchical Parallel Genetic Algorithms (HPGAs) to solve the GCP. We also propose a new extension to PGA, that is Genetic Modification (GM) operator designed for solving constraint optimization problems by taking advantage of the properties between variables and their relations. Our proposed GM for solving the GCP is based on a novel Variable Ordering Algorithm (VOA). In order to evaluate the performance of our new approach, we have conducted several experiments on GCP instances taken from the well known DIMACS website. The results show that the proposed approach has a high performance in time and quality of the solution returned in solving graph coloring instances taken from DIMACS website. The quality of the solution is measured here by comparing the returned solution with the optimal one.

Evaluation of Machine Learning Methods on a Swinging Humanoid

by Jessica Meyer

Not published

We show that, given a specific task, a variety of machine learning algorithms can be applied. The approaches are... more We show that, given a specific task, a variety of machine learning algorithms can be applied. The approaches are evaluated in terms of performance in a simulated environment and applicability to a real-world task. We argue that no approach performs optimally in all aspects considered.

Towards the Automatic Design of Decision Tree Induction Algorithms

by Rodrigo Barros

Co-authored with Marcio Basgalupp and Andre Carvalho and Alex Freitas, published in the proceedings of the 2011 GECCO, 2011.

Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation... more Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes two different approaches for automatically generating generic decision tree induction algorithms. Both approaches are based on the evolutionary algorithms paradigm, which improves solutions based on metaphors of biological processes. We also propose guidelines to design interesting fitness functions for these evolutionary algorithms, which take into account the requirements and needs of the end-user.

LEGAL-tree: a lexicographic multi-objective genetic algorithm for decision tree induction

by Rodrigo Barros

Published in the proceedings of the 24th ACM Symposium on Applied Computing

Decision trees are widely disseminated as an effective solution for classification tasks. Decision tree induction... more Decision trees are widely disseminated as an effective solution for classification tasks. Decision tree induction algorithms have some limitations though, due to the typical strategy they implement: recursive top-down partitioning through a greedy split evaluation. This strategy is limiting in the sense that there is quality loss while the partitioning process occurs, creating statistically insignificant rules. In order to prevent the greedy strategy and to avoid converging to local optima, we present a novel Genetic Algorithm for decision tree induction based on a lexicographic multi-objective approach, and we compare it with the most well-known algorithm for decision tree induction, J48, over distinct public datasets. The results show the feasibility of using this technique as a means to avoid the previously described problems, reporting not only a comparable accuracy but also, importantly, a significantly simpler classification model in the employed datasets.

Lexicographic multi-objective evolutionary induction of decision trees

by Rodrigo Barros

Published in the International Journal of Bio-inspired Computation

Among the several tasks that evolutionary algorithms have successfully employed, the induction of classification rules... more Among the several tasks that evolutionary algorithms have successfully employed, the induction of classification rules and decision trees has been shown to be a relevant approach for several application domains. Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, conventionally used decision trees induction algorithms present limitations due to the strategy they usually implement: recursive top-down data partitioning through a greedy split evaluation. The main problem with this strategy is quality loss during the partitioning process, which can lead to statistically insignificant rules. In this paper, we propose a new GA-based algorithm for decision tree induction. The proposed algorithm aims to prevent the greedy strategy and to avoid converging to local optima. For such, it is based on a lexicographic multi-objective approach. In order to evaluate the proposed algorithm, it is compared with a well-known and frequently used decision tree induction algorithm using different public datasets. According to the experimental results, the proposed algorithm is able to avoid the previously described problems, reporting accuracy gains. Even more important, the proposed algorithm induced models with a significantly reduction in the complexity considering tree sizes.

A Survey of Evolutionary Algorithms for Decision-Tree Induction

by Rodrigo Barros

Co-authored with Márcio Basgalupp, Alex Freitas and André Carvalho, published in 'IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews'

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this... more This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper’s original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.

Evolutionary Model Tree Induction

by Rodrigo Barros

MSc. dissertation

Model trees are a particular case of decision trees employed to solve regression problems, where the variable to be... more Model trees are a particular case of decision trees employed to solve regression problems, where the variable to be predicted is continuous. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the
end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is a NPComplete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this work, we propose the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in
order to improve the convergence to global optimal solutions. We test the predictive performance of this new approach using public UCI data sets, and we compare the results with traditional greedy regression/model trees induction algorithms. Results show that our approach presents a good tradeoff between predictive performance and model comprehensibility, which may be crucial in many data mining applications.

Evolutionary model tree induction

by Rodrigo Barros

Published in the proceedings of the 25th ACM Symposium on Applied Computing

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of... more Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output with an acceptable level of predictive performance. Since generating optimal model trees is a NP-Complete problem, the traditional model tree induction algorithms make use of a greedy heuristic, which may not converge to the global optimal solution. We propose the use of the evolutionary algorithms paradigm (EA) as an alternate heuristic to generate model trees in order to improve the convergence to global optimal solutions. We test the predictive performance of this new approach using public UCI datasets, and compare the results with traditional greedy regression/model trees induction algorithms.

Evolutionary design of neural architectures

by Vasant Honavar

Advances In the Evolutionary Synthesis of Intelligent Agents

by Vasant Honavar

Total Discount Policy and Two Warehouses Strategy to Store Raw Materials with Economic Order Quantity Model

by Saeid Mokaram

A. A. Taleizadeh, S. Mokaram, N. Shafii and M. Zarei, “Total Discount Policy and Two Warehouses Strategy to Store Raw Materials with Economic Order Quantity Model”, Journal of Applied Sciences, 9: 1267-1275, 2009.

This study introduced an Economic Order Quantity (EOQ) model with payment in advance to purchase high-price raw... more This study introduced an Economic Order Quantity (EOQ) model with payment in advance to purchase high-price raw materials. We relax and change some assumptions that were considered in earlier researches. At first we considered transportation cost as a linear function. Total discount policy is considered instead of incremental discount one. Also we developed model based on two warehouses strategy to store raw material in which holding cost is different for each of warehouses. We show that the model of this problem is shown to be a mixed-integer-nonlinear-programming type and in order to solve it, a simulated annealing approach is used. At the end, a numerical example is given to demonstrate the applicability of the proposed methodology in real world inventory control problems.

A novel hybrid genetic algorithm for the open shop scheduling problem

by Mehdi Hosseinabadi Farahani

International Journal of Advanced Manufacturing Technology
DOI 10.1007/s00170-011-3825-1

In this paper, a hybrid genetic algorithm is proposed for the open shop scheduling problem with the objective of... more In this paper, a hybrid genetic algorithm is proposed for the open shop scheduling problem with the objective of minimizing the makespan. In the proposed algorithm, a specialized crossover operator is used that preserves the relative order of jobs on machines and a strategy is applied to prevent from searching redundant solutions in the mutation operator. Moreover, an iterative optimization heuristic is employed which uses the concept of randomized active schedules, a dispatching index based on the longest remaining processing time rule and a lower bound to further decrease the search space. Computational results show that the proposed algorithm outperforms other genetic algorithms and is very competitive with well-known metaheuristics available in the literature.

Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation

by Jose A. Fernandez

This paper deals with the study of scaling up behaviors in evolutive robotics (ER). Complex behaviours were obtained... more This paper deals with the study of scaling up behaviors in evolutive robotics (ER). Complex behaviours were obtained from simple ones. Each behavior is supported by an artificial neural network (ANN)-based controller or neurocontroller. Hence, a method for the generation of a hierarchy of neurocontrollers, resorting to the paradigm of Layered Evolution (LE), is developed and verified experimentally through computer simulations and tests in a Kheperamicro-robot. Several behavioral modules are initially evolved using specialized neurocontrollers based on different ANN paradigms. The results show that simple behaviors coordination through LE is a feasible strategy that gives rise to emergent complex behaviors. These complex behaviors can then solve real-world problems efficiently. From a pure evolutionary perspective, however, the methodology presented is too much dependent on user’s prior knowledge about the problem to solve and also that evolution take place in a rigid, prescribed framework. Mobile robot’s navigation in an unknown environment is used as a test bed for the proposed scaling strategies.

Behavioral Robustness: an Emergent Phenomenon by means of Distributed Mechanisms and Neurodynamic Determinacy

by Jose A. Fernandez

Theoretical discussions and computational models of bio-inspired embodied and situated agents are introduced in this... more Theoretical discussions and computational models of bio-inspired embodied and situated agents are introduced in this article capturing in simplified form the dynamical essence of robust, yet adaptive behavior. This article analyzes the general problem of how the dynamical coupling between internal control (brain), body and environment is used in the generation of specific behaviors. Based on the Evolutionary Robotics (ER) paradigm, four computational models are described to support discussions including descriptions on performance after a series of structural, sensorimotor or mutational perturbations, or are developed in the absence of them. Experimental results suggest that ‘dynamic determinacy’ – i.e. the continuous presence of a unique dynamical attractor that must be chased during functional behaviours – is a common dynamic phenomenon in the analyzed robust and adaptive agents. These agents show dynamical states that are definitely and unequivocally characterized via transient dynamics toward a unique, yet moving attractor at neural level for coherent actions. This determinacy emerges as a control strategy rooted on behavioral couplings and relies on mechanisms that are distributed on brain, body and environment. Different ways to induce further distribution of behavioral mechanisms are also discussed in this paper from a bio-inspired ER perspective.