2,203 research outputs found
Decision-making Tools and Memetic Algorithms in Management and Linear Programming Problems
Operational Research uses a set of tools based on scientific research principles to achieve rational and meaningful management decisions. This article tries to give solution to a highly complex Linear Programming problem by using Simplex method, Solver and a hybrid prototype which combines the theories of Genetic Algorithms with a new local search heuristic technique. Hybridization of these two techniques is becoming known as Memetic Algorithm. Additionally, this article tries to present different techniques to support management decision-making, with the intention of being used increasingly in the business environment sustaining, thus, decisions by mathematics or artificial intelligence and not only by experience.quantitative management; quantitative methods; decision-making; linear programming; operational research; heuristics; hybrid methods; memetic algorithms.
Myths and Legends of the Baldwin Effect
This position paper argues that the Baldwin effect is widely
misunderstood by the evolutionary computation community. The
misunderstandings appear to fall into two general categories.
Firstly, it is commonly believed that the Baldwin effect is
concerned with the synergy that results when there is an evolving
population of learning individuals. This is only half of the story.
The full story is more complicated and more interesting. The Baldwin
effect is concerned with the costs and benefits of lifetime
learning by individuals in an evolving population. Several
researchers have focussed exclusively on the benefits, but there
is much to be gained from attention to the costs. This paper explains
the two sides of the story and enumerates ten of the costs and
benefits of lifetime learning by individuals in an evolving population.
Secondly, there is a cluster of misunderstandings about the relationship
between the Baldwin effect and Lamarckian inheritance of acquired
characteristics. The Baldwin effect is not Lamarckian. A Lamarckian
algorithm is not better for most evolutionary computing problems than
a Baldwinian algorithm. Finally, Lamarckian inheritance is not a
better model of memetic (cultural) evolution than the Baldwin effect
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