1,112 research outputs found
Decision Making in the Medical Domain: Comparing the Effectiveness of GP-Generated Fuzzy Intelligent Structures
ABSTRACT: In this work, we examine the effectiveness of two intelligent models in medical domains. Namely, we apply grammar-guided genetic programming to produce fuzzy intelligent structures, such as fuzzy rule-based systems and fuzzy Petri nets, in medical data mining tasks. First, we use two context-free grammars to describe fuzzy rule-based systems and fuzzy Petri nets with genetic programming. Then, we apply cellular encoding in order to express the fuzzy Petri nets with arbitrary size and topology. The models are examined thoroughly in four real-world medical data sets. Results are presented in detail and the competitive advantages and drawbacks of the selected methodologies are discussed, in respect to the nature of each application domain. Conclusions are drawn on the effectiveness and efficiency of the presented approach
High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization
DisertaÄŤnĂ práce je zaměřena na optimalizaci prĹŻbÄ›hu pracovnĂch operacĂ v logistickĂ˝ch skladech a distribuÄŤnĂch centrech. HlavnĂm cĂlem je optimalizovat procesy plánovánĂ, rozvrhovánĂ a odbavovánĂ. JelikoĹľ jde o problĂ©m patĹ™ĂcĂ do tĹ™Ădy sloĹľitosti NP-teĹľkĂ˝, je vĂ˝poÄŤetnÄ› velmi nároÄŤnĂ© nalĂ©zt optimálnĂ Ĺ™ešenĂ. MotivacĂ pro Ĺ™ešenĂ tĂ©to práce je vyplnÄ›nĂ pomyslnĂ© mezery mezi metodami zkoumanĂ˝mi na vÄ›deckĂ© a akademickĂ© pĹŻdÄ› a metodami pouĹľĂvanĂ˝mi v produkÄŤnĂch komerÄŤnĂch prostĹ™edĂch. Jádro optimalizaÄŤnĂho algoritmu je zaloĹľeno na základÄ› genetickĂ©ho programovánĂ Ĺ™ĂzenĂ©ho bezkontextovou gramatikou. HlavnĂm pĹ™Ănosem tĂ©to práce je a) navrhnout novĂ˝ optimalizaÄŤnĂ algoritmus, kterĂ˝ respektuje následujĂcĂ optimalizaÄŤnĂ podmĂnky: celkovĂ˝ ÄŤas zpracovánĂ, vyuĹľitĂ zdrojĹŻ, a zahlcenĂ skladovĂ˝ch uliÄŤek, kterĂ© mĹŻĹľe nastat bÄ›hem zpracovánĂ ĂşkolĹŻ, b) analyzovat historická data z provozu skladu a vyvinout sadu testovacĂch pĹ™ĂkladĹŻ, kterĂ© mohou slouĹľit jako referenÄŤnĂ vĂ˝sledky pro dalšà vĂ˝zkum, a dále c) pokusit se pĹ™edÄŤit stanovenĂ© referenÄŤnĂ vĂ˝sledky dosaĹľenĂ© kvalifikovanĂ˝m a trĂ©novanĂ˝m operaÄŤnĂm manaĹľerem jednoho z nejvÄ›tšĂch skladĹŻ ve stĹ™ednĂ EvropÄ›.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.
Evolutionary generation of fuzzy knowledge bases for diagnosing monitored railway systems
Classical approaches when building diagnosis and monitoring systems are rule-based systems, which allow the representation of existing knowledge by using rules. There are several techniques that facilitate this task, such as fuzzy logic, which allows knowledge to be modeled in an intuitive way. Nevertheless, sometimes it is not easy to define the fuzzy rule set that represents the knowledge from a certain domain. To overcome this drawback, an evolutionary system based on a grammar guided genetic programming technique for the automatic generation of fuzzy knowledge bases has been employed in diagnosing monitored railway networks. This system employs a grammar-based initialization method and both, grammar-based crossover and mutation operators, to achieve well balanced exploitation and exploration capabilities of the search space, assuring high convergence speed and low chance of getting trapped in local optima. Tests have been carried out in a real-world train monitoring domain, in which a sensor network is periodically monitoring critical train components. Results achieved show that this evolutionary system accomplishes an automatic knowledge discovery process, which is able to build a fuzzy rule base that represents the expert knowledge extracted from the domain of the detection of abnormal train conditions
Using Domain Knowledge in Evolutionary System Identification
Two example of Evolutionary System Identification are presented to highlight the importance of incorportaing Domain Knowledge: the discovery of an analytical indentation law in Structural Mechanics using constrained Genetic Programming, and the identification of the repartition of underground velocities in Seimsi Prospection. Critical issues for sucessful ESI are discussed in the light of these results
Grammar-based Representation and Identification of Dynamical Systems
In this paper we propose a novel approach to identify dynamical systems. The
method estimates the model structure and the parameters of the model
simultaneously, automating the critical decisions involved in identification
such as model structure and complexity selection. In order to solve the
combined model structure and model parameter estimation problem, a new
representation of dynamical systems is proposed. The proposed representation is
based on Tree Adjoining Grammar, a formalism that was developed from linguistic
considerations. Using the proposed representation, the identification problem
can be interpreted as a multi-objective optimization problem and we propose a
Evolutionary Algorithm-based approach to solve the problem. A benchmark example
is used to demonstrate the proposed approach. The results were found to be
comparable to that obtained by state-of-the-art non-linear system
identification methods, without making use of knowledge of the system
description.Comment: Submitted to European Control Conference (ECC) 201
Multi-level diversity promotion strategies for Grammar-guided Genetic Programming
Grammar-guided Genetic Programming (G3P) is a family of Evolutionary Algorithms that can evolve programs in any language described by a context-free grammar. The most widespread members of this family are based on an indirect representation: a sequence of bits or integers (the genotype) is transformed into a string of the language (the phenotype) by means of a mapping function, and eventually into a fitness value. Unfortunately, the flexibility brought by this mapping is also likely to introduce non-locality phenomena, reduce diversity, and hamper the effectiveness of the algorithm. In this paper, we experimentally characterize how population diversity, measured at different levels, varies for four popular G3P approaches. We then propose two strategies for promoting diversity which are general, independent both from the specific problem being tackled and from the other components of the Evolutionary Algorithm, such as genotype-phenotype mapping, selection criteria, and genetic operators. We experimentally demonstrate their efficacy in a wide range of conditions and from different points of view. The results also confirm the preponderant importance of the phenotype-level analyses in diversity promotion
Towards the automatic generation of card games through Grammar-Guided Genetic Programming
We demonstrate generating complete and playable card games using evolutionary algorithms. Card games are represented in a previously devised card game description language, a context-free grammar. The syntax of this language allows us to use grammar-guided genetic programming. Candidate card games are evaluated through a cascading evaluation function, a multi-step process where games with undesired properties are progressively weeded out. Three representa- tive examples of generated games are analysed. We observed that these games are reasonably balanced and have skill ele- ments, they are not yet entertaining for human players. The particular shortcomings of the examples are discussed in re- gard to the generative process to be able to generate quality game
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