A connectionist machine for genetic hillclimbing

by David H. Ackley

[目次]

  • 1. Introduction.- 1.1. Satisfying hidden strong constraints.- 1.2. Function optimization.- 1.2.1. The methodology of heuristic search.- 1.2.2. The shape of function spaces.- 1.3. High-dimensional binary vector spaces.- 1.3.1. Graph partitioning.- 1.4. Dissertation overview.- 1.5. Summary.- 2. The model.- 2.1. Design goal: Learning while searching.- 2.1.1. Knowledge representation.- 2.1.2. Point-based search strategies.- 2.1.3. Population-based search strategies.- 2.1.4. Combination rules.- 2.1.5. Election rules.- 2.1.6. Summary: Learning while searching.- 2.2. Design goal: Sustained exploration.- 2.2.1. Searching broadly.- 2.2.2. Convergence and divergence.- 2.2.3. Mode transitions.- 2.2.4. Resource allocation via taxation.- 2.2.5. Summary: Sustained exploration.- 2.3. Connectionist computation.- 2.3.1. Units and links.- 2.3.2. A three-state stochastic unit.- 2.3.3. Receptive fields.- 2.4. Stochastic iterated genetic hillclimbing.- 2.4.1. Knowledge representation in SIGH.- 2.4.2. The SIGH control algorithm.- 2.4.3. Formal definition.- 2.5. Summary.- 3. Empirical demonstrations.- 3.1. Methodology.- 3.1.1. Notation.- 3.1.2. Parameter tuning.- 3.1.3. Non-termination.- 3.2. Seven algorithms.- 3.2.1. Iterated hillclimbing-steepest ascent (IHC-SA).- 3.2.2. Iterated hillclimbing-next ascent (IHC-NA).- 3.2.3. Stochastic hillclimbing (SHC).- 3.2.4. Iterated simulated annealing (ISA).- 3.2.5. Iterated genetic search-Uniform combination (IGS-U).- 3.2.6. Iterated genetic search-Ordered combination (IGS-O).- 3.2.7. Stochastic iterated genetic hillclimbing (SIGH).- 3.3. Six functions.- 3.3.1. A linear space-"One Max".- 3.3.2. A local maximum-"Two Max".- 3.3.3. A large local maximum-"Trap".- 3.3.4. Fine-grained local maxima-"Porcupine".- 3.3.5. Flat areas-"Plateaus".- 3.3.6. A combination space-"Mix".- 4. Analytic properties.- 4.1. Problem definition.- 4.2. Energy functions.- 4.3. Basic properties of the learning algorithm.- 4.3.1. Motivating the approach.- 4.3.2. Defining reinforcement signals.- 4.3.3. Defining similarity measures.- 4.3.4. The equilibrium distribution.- 4.4. Convergence.- 4.5. Divergence.- 5. Graph partitioning.- 5.1. Methodology.- 5.1.1. Problems.- 5.1.2. Algorithms.- 5.1.3. Data collection.- 5.1.4. Parameter tuning.- 5.2. Adding a linear component.- 5.3. Experiments on random graphs.- 5.4. Experiments on multilevel graphs.- 6. Related work.- 6.1. The problem space formulation.- 6.2. Search and learning.- 6.2.1. Learning while searching.- 6.2.2. Symbolic learning.- 6.2.3. Hillclimbing.- 6.2.4. Stochastic hillclimbing and simulated annealing.- 6.2.5. Genetic algorithms.- 6.3. Connectionist modelling.- 6.3.1. Competitive learning.- 6.3.2. Back propagation.- 6.3.3. Boltzmann machines.- 6.3.4. Stochastic iterated genetic hillclimbing.- 6.3.5. Harmony theory.- 6.3.6. Reinforcement models.- 7. Limitations and variations.- 7.1. Current limitations.- 7.1.1. The problem.- 7.1.2. The SIGH model.- 7.2. Possible variations.- 7.2.1. Exchanging parameters.- 7.2.2. Beyond symmetric connections.- 7.2.3. Simultaneous optimization.- 7.2.4. Widening the bottleneck.- 7.2.5. Temporal credit assignment.- 7.2.6. Learning a function.- 8. Discussion and conclusions.- 8.1. Stability and change.- 8.2. Architectural goals.- 8.2.1 High potential parallelism.- 8.2.2 Highly incremental.- 8.2.3 "Generalized Hebbian" learning.- 8.2.4 Unsupervised learning.- 8.2.5 "Closed loop" interactions.- 8.2.6 Emergent properties.- 8.3. Discussion.- 8.3.1 The processor/memory distinction.- 8.3.2 Physical computation systems.- 8.3.3 Between mind and brain.- 8.4. Conclusions.- 8.4.1. Recapitulation.- 8.4.2. Contributions.- References.

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この本の情報

書名 A connectionist machine for genetic hillclimbing
著作者等 Ackley, David H.
シリーズ名 The Kluwer international series in engineering and computer science
出版元 Kluwer Academic
刊行年月 c1987
ページ数 xii, 260 p.
大きさ 25 cm
ISBN 089838236X
NCID BA01260339
※クリックでCiNii Booksを表示
言語 英語
出版国 アメリカ合衆国
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