7,167 research outputs found
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.
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Machine learning : techniques and foundations
The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples
Synthesis of Parametric Programs using Genetic Programming and Model Checking
Formal methods apply algorithms based on mathematical principles to enhance
the reliability of systems. It would only be natural to try to progress from
verification, model checking or testing a system against its formal
specification into constructing it automatically. Classical algorithmic
synthesis theory provides interesting algorithms but also alarming high
complexity and undecidability results. The use of genetic programming, in
combination with model checking and testing, provides a powerful heuristic to
synthesize programs. The method is not completely automatic, as it is fine
tuned by a user that sets up the specification and parameters. It also does not
guarantee to always succeed and converge towards a solution that satisfies all
the required properties. However, we applied it successfully on quite
nontrivial examples and managed to find solutions to hard programming
challenges, as well as to improve and to correct code. We describe here several
versions of our method for synthesizing sequential and concurrent systems.Comment: In Proceedings INFINITY 2013, arXiv:1402.661
Evolutionary Algorithms for Reinforcement Learning
There are two distinct approaches to solving reinforcement learning problems,
namely, searching in value function space and searching in policy space.
Temporal difference methods and evolutionary algorithms are well-known examples
of these approaches. Kaelbling, Littman and Moore recently provided an
informative survey of temporal difference methods. This article focuses on the
application of evolutionary algorithms to the reinforcement learning problem,
emphasizing alternative policy representations, credit assignment methods, and
problem-specific genetic operators. Strengths and weaknesses of the
evolutionary approach to reinforcement learning are presented, along with a
survey of representative applications
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Genetic algorithm approach to find the best input variable partitioning
Conference PaperThis paper presents a variable partition algorithm which combines the quasi-reduced ordered multiple-terminal multiple-valued decision diagrams and genetic algorithms (GAs). The algorithm is better than the previous techniques which find a good functional decomposition by non-exhaustive search and expands the range of searching for the best decomposition providing the optimal subtable multiplicity. The possible solutions are evaluated using the gain of decomposition for a multiple-output multiple-valued logic function. The distinct feature of GA is the possible solutions being coded by real numbers. Here the simplex-based crossover is proposed to use for the recombination stage of GA. It permits to increase the GA coverag
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