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ICARUS: Intelligent coupon allocation for retailers using search
Many retailers run loyalty card schemes for their customers offering incentives in the form of money off coupons. The total value of the coupons depends on how much the customer has spent. This paper deals with the problem of finding the smallest set of coupons such that each possible total can be represented as the sum of a pre-defined number of coupons. A mathematical analysis of the problem leads to the development of a genetic algorithm solution. The algorithm is applied to real world data using several crossover operators and compared to well known straw-person methods. Results are promising showing that considerable time can be saved by using this method, reducing a few days worth of consultancy time to a few minutes of computation
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
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
Evolutionary Learning of Hierarchical Decision Rules
This paper describes an approach based on evolutionary
algorithms, hierarchical decision rules (HIDER), for
learning rules in continuous and discrete domains. The algorithm
produces a hierarchical set of rules, that is, the rules are sequentially
obtained and must be, therefore, tried in order until one is
found whose conditions are satisfied. Thus, the number of rules
may be reduced because the rules could be inside one another.
The evolutionary algorithm uses both real and binary coding for
the individuals of the population. We have tested our system on
real data from the UCI Repository, and the results of a ten-fold
cross-validation are compared to C4.5s, C4.5Rules, See5s, and
See5Rules. The experiments show that HIDER works well in
practice
Genetic Programming + Unfolding Embryology in Automated Layout Planning
Automated layout planning aims to the implementation of computational methods for the generation and the optimization of floor plans, considering the spatial configuration and the assignment of activities. Sophisticated strategies such as Genetic Algorithms have been implemented as heuristics of good solutions. However, the generative forces that derive from the social structures have been often neglected. This research aims to illustrate that the data that encode the layoutâs social and cultural generative forces, can be implemented within an evolutionary system for the design of residential layouts. For that purpose a co-operative system was created, which is composed of a Genetic Programming algorithm and an agent-based unfolding embryology procedure that assigns activities to the spaces generated by the GP algorithm. The assignment of activities is a recursive process which follows instructions encoded as permeability graphs. Furthermore, the Ranking Sum Fitness evaluation method is proposed and applied for the achievement of multi-objective optimization. Its efficiency is tested against the Weighted-Sum Fitness function. The systemâs results, both numerical and spatial, are compared to the results of a conventional evolutionary approach. This comparison showed that, in general, the proposed system can yield better solutions
The Algorithmic Origins of Life
Although it has been notoriously difficult to pin down precisely what it is
that makes life so distinctive and remarkable, there is general agreement that
its informational aspect is one key property, perhaps the key property. The
unique informational narrative of living systems suggests that life may be
characterized by context-dependent causal influences, and in particular, that
top-down (or downward) causation -- where higher-levels influence and constrain
the dynamics of lower-levels in organizational hierarchies -- may be a major
contributor to the hierarchal structure of living systems. Here we propose that
the origin of life may correspond to a physical transition associated with a
shift in causal structure, where information gains direct, and
context-dependent causal efficacy over the matter it is instantiated in. Such a
transition may be akin to more traditional physical transitions (e.g.
thermodynamic phase transitions), with the crucial distinction that determining
which phase (non-life or life) a given system is in requires dynamical
information and therefore can only be inferred by identifying causal
architecture. We discuss some potential novel research directions based on this
hypothesis, including potential measures of such a transition that may be
amenable to laboratory study, and how the proposed mechanism corresponds to the
onset of the unique mode of (algorithmic) information processing characteristic
of living systems.Comment: 13 pages, 1 tabl
Learning in evolutionary environments
Not availabl
Learning in Evolutionary Environments
The purpose of this work is to present a sort of short selective guide to an enormous and diverse literature on learning processes in economics. We argue that learning is an ubiquitous characteristic of most economic and social systems but it acquires even greater importance in explicitly evolutionary environments where: a) heterogeneous agents systematically display various forms of "bounded rationality"; b) there is a persistent appearance of novelties, both as exogenous shocks and as the result of technological, behavioural and organisational innovations by the agents themselves; c) markets (and other interaction arrangements) perform as selection mechanisms; d) aggregate regularities are primarily emergent properties stemming from out-of-equilibrium interactions. We present, by means of examples, the most important classes of learning models, trying to show their links and differences, and setting them against a sort of ideal framework of "what one would like to understand about learning...". We put a signifiphasis on learning models in their bare-bone formal structure, but we also refer to the (generally richer) non-formal theorising about the same objects. This allows us to provide an easier mapping of a wide and largely unexplored research agenda.Learning, Evolutionary Environments, Economic Theory, Rationality
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