76,508 research outputs found
Repeated sequences in linear genetic programming genomes
Biological chromosomes are replete with repetitive sequences, micro
satellites, SSR tracts, ALU, etc. in their DNA base sequences. We
started looking for similar phenomena in evolutionary computation.
First studies find copious repeated sequences, which can be hierarchically
decomposed into shorter sequences, in programs evolved using
both homologous and two point crossover but not with headless chicken
crossover or other mutations. In bloated programs the small number
of effective or expressed instructions appear in both repeated and nonrepeated
code. Hinting that building-blocks or code reuse may evolve
in unplanned ways.
Mackey-Glass chaotic time series prediction and eukaryotic protein
localisation (both previously used as artificial intelligence machine
learning benchmarks) demonstrate evolution of Shannon information
(entropy) and lead to models capable of lossy Kolmogorov compression.
Our findings with diverse benchmarks and GP systems suggest
this emergent phenomenon may be widespread in genetic systems
Repeated patterns in tree genetic programming
We extend our analysis of repetitive patterns found in genetic programming genomes to tree based GP.
As in linear GP, repetitive patterns are present in large numbers. Size fair crossover limits bloat in automatic programming, preventing the evolution of recurring motifs. We examine these complex properties in detail: e.g. using depth v. size Catalan binary tree shape plots, subgraph and subtree matching, information entropy, syntactic and semantic fitness correlations and diffuse introns. We relate this emergent phenomenon to considerations about building blocks in GP and how GP works
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.
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