Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context

A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue. Therefore, the aim is to make firms evolve from classical sequential approach (first product design the project design and management) to new integrated approaches. In this paper, a model for integrated product/project optimization is first proposed which allows taking into account simultaneously decisions coming from the product and project managers. However, the resulting model has an important underlying complexity, and a multi-objective optimization technique is required to provide managers with appropriate scenarios in a reasonable amount of time. The proposed approach is based on an original evolutionary algorithm called evolutionary algorithm oriented by knowledge (EAOK). This algorithm is based on the interaction between an adapted evolutionary algorithm and a model of knowledge (MoK) used for giving relevant orientations during the search process. The evolutionary operators of the EA are modified in order to take into account these orientations. The MoK is based on the Bayesian Network formalism and is built both from expert knowledge and from individuals generated by the EA. A learning process permits to update probabilities of the BN from a set of selected individuals. At each cycle of the EA, probabilities contained into the MoK are used to give some bias to the new evolutionary operators. This method ensures both a faster and effective optimization, but it also provides the decision maker with a graphic and interactive model of knowledge linked to the studied project. An experimental platform has been developed to experiment the algorithm and a large campaign of tests permits to compare different strategies as well as the benefits of this novel approach in comparison with a classical EA

A review of estimation of distribution algorithms in bioinformatics

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain

Un algoritmo de estimación de distribución para solucionar problemas de programación en ambiente flowshop con bloqueo y con múltiples objetivos

La programación de producción tiene un impacto relevante sobre el uso eficiente de los recursos, reducción de costos y cumplimiento de los objetivos como servicio al cliente, entregas oportunas y satisfacción de la demanda. En un entorno cada vez más competitivo, las organizaciones se ven en la necesidad de aplicar herramientas, procedimientos y estrategias que les permitan estar a la vanguardia. En ese sentido, el uso de las metaheurísticas para resolver problemas de programación y secuenciación de trabajos va en aumento, ya que se han demostrado sus fortalezas para la búsqueda de soluciones eficientes, oportunas, rápidas y de buena calidad. Adicionalmente, las organizaciones buscan satisfacer o cumplir varios objetivos o metas de manera simultánea como entregar a tiempo al mínimo costo, entre otros. Así, se propone y desarrolla un metaheurístico de estimación de distribución para un ambiente de programación tipo flowshop con restricciones de bloqueo y con múltiples objetivos. A partir de la experimentación, se evidencia un adecuado rendimiento del algoritmo en cuanto a las soluciones encontradas y al rendimiento, que no se ve afectado por el número de trabajos, ni de máquinas a considerar en el problemaAbstract: The production scheduling has a relevant impact on the efficient use of resources, reduction of costs and fulfilment of the objectives such as customer service, timely deliveries and demand satisfaction. In an increasingly competitive environment, organizations are in the need for tools, procedures and strategies that allow them to be at the forefront. In this sense, the use of metaheuristics for solving problems of scheduling and sequencing of jobs is increasing, since their strengths aiming to pursuit fast, timely, efficient and of good quality solutions, have been shown. In addition, organizations seek to meet several objectives or goals simultaneously, such as on time and the minimum cost deliveries, among others. Thus, an estimation of distribution metaheuristic for a flowshop scheduling problem with blocking and multiple objectives is proposed and developed. As a result of the experimentation, there is evidence of an appropriate performance of the algorithm in terms of the solutions found and the performance, which is not affected by the number of jobs or machines to be considered in the problemMaestrí

Using Prior Knowledge and Learning from Experience in Estimation of Distribution Algorithms

Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. One of the primary advantages of EDAs over many other stochastic optimization techniques is that after each run they leave behind a sequence of probabilistic models describing useful decompositions of the problem. This sequence of models can be seen as a roadmap of how the EDA solves the problem. While this roadmap holds a great deal of information about the problem, until recently this information has largely been ignored. My thesis is that it is possible to exploit this information to speed up problem solving in EDAs in a principled way. The main contribution of this dissertation will be to show that there are multiple ways to exploit this problem-specific knowledge. Most importantly, it can be done in a principled way such that these methods lead to substantial speedups without requiring parameter tuning or hand-inspection of models

Using previous models to bias structural learning in the hierarchical BOA

Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. While the primary goal of applying EDAs is to discover the global optimum or at least its accurate approximation, besides this, any EDA provides us with a sequence of probabilistic models, which in most cases hold a great deal of information about the problem. Although using problem-specific knowledge has been shown to significantly improve performance of EDAs and other evolutionary algorithms, this readily available source of problem-specific information has been practically ignored by the EDA community. This paper takes the first step towards the use of probabilistic models obtained by EDAs to speed up the solution of similar problems in future. More specifically, we propose two approaches to biasing model building in the hierarchical Bayesian optimization algorithm (hBOA) based on knowledge automatically learned from previous hBOA runs on similar problems. We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure