15,665 research outputs found
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies
An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topology and initial weights, have proven to be effective at
exploiting domain-specific knowledge; however, most do not exploit available
computing power. This weakness occurs because they lack the ability to refine
the topology of the neural networks they produce, thereby limiting
generalization, especially when given impoverished domain theories. We present
the REGENT algorithm which uses (a) domain-specific knowledge to help create an
initial population of knowledge-based neural networks and (b) genetic operators
of crossover and mutation (specifically designed for knowledge-based networks)
to continually search for better network topologies. Experiments on three
real-world domains indicate that our new algorithm is able to significantly
increase generalization compared to a standard connectionist theory-refinement
system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file
Pre-emptive resource-constrained multimode project scheduling using genetic algorithm: a dynamic forward approach
Purpose: The issue resource over-allocating is a big concern for project engineers in the process
of scheduling project activities. Resource over-allocating drawback is frequently seen after
scheduling of a project in practice which causes a schedule to be useless. Modifying an
over-allocated schedule is very complicated and needs a lot of efforts and time. In this paper, a
new and fast tracking method is proposed to schedule large scale projects which can help project
engineers to schedule the project rapidly and with more confidence.
Design/methodology/approach: In this article, a forward approach for maximizing net
present value (NPV) in multi-mode resource constrained project scheduling problem while
assuming discounted positive cash flows (MRCPSP-DCF) is proposed. The progress payment
method is used and all resources are considered as pre-emptible. The proposed approach
maximizes NPV using unscheduled resources through resource calendar in forward mode. For
this purpose, a Genetic Algorithm is applied to solve.
Findings: The findings show that the proposed method is an effective way to maximize NPV in
MRCPSP-DCF problems while activity splitting is allowed. The proposed algorithm is very fast
and can schedule experimental cases with 1000 variables and 100 resources in few seconds. The
results are then compared with branch and bound method and simulated annealing algorithm and
it is found the proposed genetic algorithm can provide results with better quality. Then algorithm
is then applied for scheduling a hospital in practice.
Originality/value: The method can be used alone or as a macro in Microsoft Office Project®
Software to schedule MRCPSP-DCF problems or to modify resource over-allocated activities
after scheduling a project. This can help project engineers to schedule project activities rapidly
with more accuracy in practice.Peer Reviewe
CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features
In this paper we propose a crossover operator for evolutionary algorithms
with real values that is based on the statistical theory of population
distributions. The operator is based on the theoretical distribution of the
values of the genes of the best individuals in the population. The proposed
operator takes into account the localization and dispersion features of the
best individuals of the population with the objective that these features would
be inherited by the offspring. Our aim is the optimization of the balance
between exploration and exploitation in the search process. In order to test
the efficiency and robustness of this crossover, we have used a set of
functions to be optimized with regard to different criteria, such as,
multimodality, separability, regularity and epistasis. With this set of
functions we can extract conclusions in function of the problem at hand. We
analyze the results using ANOVA and multiple comparison statistical tests. As
an example of how our crossover can be used to solve artificial intelligence
problems, we have applied the proposed model to the problem of obtaining the
weight of each network in a ensemble of neural networks. The results obtained
are above the performance of standard methods
Practical Distributed Control Synthesis
Classic distributed control problems have an interesting dichotomy: they are
either trivial or undecidable. If we allow the controllers to fully
synchronize, then synthesis is trivial. In this case, controllers can
effectively act as a single controller with complete information, resulting in
a trivial control problem. But when we eliminate communication and restrict the
supervisors to locally available information, the problem becomes undecidable.
In this paper we argue in favor of a middle way. Communication is, in most
applications, expensive, and should hence be minimized. We therefore study a
solution that tries to communicate only scarcely and, while allowing
communication in order to make joint decision, favors local decisions over
joint decisions that require communication.Comment: In Proceedings INFINITY 2011, arXiv:1111.267
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