preprints available: optimization & neural nets

Roberto Battiti battiti at volterra.science.unitn.it
Mon May 9 10:23:32 EDT 1994


                       *** PREPRINTS AVAILABLE: ***
                        OPTIMIZATION & NEURAL NETS

The following technical reports are available by anonymous ftp at our local 
archive: volterra.science.unitn.it (130.186.34.16). 
The subjects are combinatorial and continuous optimization algorithms, and 
their application to neural nets.
Two papers (battiti.neuro-hep.ps.Z, battiti.reactive-tabu-search.ps.Z)
are also available from the neuroprose archive.

A limited number of hardcopies can be obtained from:

  Roberto Battiti
  Dip. di Matematica Univ. di Trento
  38050 Povo (Trento) - ITALY
  email: battiti at science.unitn.it
or:
  Giampietro Tecchiolli
  Istituto per la Ricerca Scientifica e Tecnologica
  38050 Povo (Trento) - ITALY
  email: tec at irst.it

________________________________________________________________________________
 ARCHIVE-NN-1
        title: 	The Reactive Tabu Search
        author:	Roberto Battiti and Giampietro Tecchiolli
	number:  UTM 405 Ottobre 1992
	note:	27 pages, to appear in: ORSA Journal on Computing, 1994
 	abstract:  
 We propose an algorithm for combinatorial optimization where an explicit 
 check for the repetition of configurations is added to the basic scheme of 
 Tabu search.  In our  Tabu scheme the appropriate size of the list is learned 
 in an automated way by reacting to the occurrence of cycles. In addition, if 
 the search appears to be repeating an excessive number of solutions excessively
 often, then the search is diversified by making a number of random moves 
 proportional to a moving average of the cycle length.
 The reactive scheme is compared to a strict Tabu scheme, that forbids the 
 repetition of configurations and to schemes with a fixed or randomly varying 
 list size.  From the implementation point of view we show that the Hashing or
 Digital Tree techniques can be used in order to search for repetitions in a 
 time that is approximately constant.
 We present the results obtained for a series of computational tests on a 
 benchmark function, on the 0-1 Knapsack Problem, and on the Quadratic 
 Assignment Problem.
	FTP-host: volterra.science.unitn.it
        FTP-file: pub/neuronit/reactive-tabu-search.ps.Z
________________________________________________________________________________
 ARCHIVE-NN-2
        title: 	Local Search with Memory: Benchmarking RTS
        author:	Roberto Battiti and Giampietro Tecchiolli
	number:  UTM Ottobre 1993
	note:	34 pages
 	abstract:  
 The purpose of this work is that of presenting a version of the Reactive Tabu
 Search method  (RTS) that is suitable for constrained problems, and that of 
 testing RTS on a series of constrained and unconstrained Combinatorial Optimi-
 zation tasks.  The benchmark suite consists of many instances of the N-K model
 and of the Knapsack problem with various sizes and difficulties, defined with
 portable random number generators.
 The performance of RTS is compared with that of Repeated Local Minima Search,
 Simulated Annealing, Genetic Algorithms, and Neural Networks.  In addition, 
 the effects of different hashing schemes and of the presence of a simple
 `aspiration' criterion in the RTS algorithm are investigated.
	FTP-host: volterra.science.unitn.it
        FTP-file: pub/neuronit/rts-benchmark.ps.Z
________________________________________________________________________________
 ARCHIVE-NN-3
        title: 	Training Neural Nets with the Reactive Tabu Search
        author:	Roberto Battiti and Giampietro Tecchiolli
	number:  UTM 421 Novembre 1993
	note:	45 pages, shorter version to appear in: 
					IEEE Trans. on Neural Networks
 	abstract:  
 In this paper the task of training sub-symbolic systems is considered as a 
 combinatorial optimization problem and solved with the heuristic scheme of 
 the Reactive Tabu Search (RTS) proposed by the authors and based on F. Glover's
 Tabu Search.  An iterative optimization process based on a  ``modified greedy 
 search'' component is complemented with a meta-strategy to realize a discrete 
 dynamical system that discourages limit cycles and the confinement of the 
 search trajectory in a limited portion of the search space. The possible 
 cycles are discouraged by prohibiting (i.e., making tabu) the execution of 
 moves that reverse the ones applied in the most recent part of the search, 
 for a prohibition period that is adapted in an automated way.  The confinement
 is avoided and a proper exploration is obtained by activating a diversification
 strategy  when too many configurations are repeated excessively often.
 The RTS method is applicable to non-differentiable functions, it is robust 
 with respect to the random initialization and effective in continuing the 
 search after local minima. The limited memory and processing required make RTS
 a competitive candidate for special-purpose VLSI implementations. We present 
 and discuss four tests of the technique on feedforward and feedback systems.
	FTP-host: volterra.science.unitn.it
        FTP-file: pub/neuronit/rts-neural-nets.ps.Z
________________________________________________________________________________
 ARCHIVE-NN-4
        title: 	Learning with first, second, and no derivatives:
		   a case study in High Energy Physics
        author:	Roberto Battiti and Giampietro Tecchiolli
	note:	36 pages, to appear in Neurocomputing 6, 181-206, 1994
 	abstract:  
 In this paper different algorithms for training multi-layer perceptron
 architectures are applied to a significant discrimination task in High Energy
 Physics. The One Step Secant technique is compared with On-Line Backpropagation
 , the 'Bold Driver' batch version and Conjugate Gradient methods. In addition,
 a new algorithm (Affine Shaker) is proposed that uses stochastic search based
 on function values and affine transformations of the local search region. 
 Although the Affine Shaker requires more CPU time to reach the maximum genera-
 lization, the technique can be interesting for special-purpose VLSI implementa-
 tions and for non-differentiable functions.
	FTP-host: volterra.science.unitn.it
        FTP-file: pub/neuronit/neuro-hep.ps.Z
________________________________________________________________________________
 ARCHIVE-NN-5
        title: 	Simulated Annealing and Tabu Search in the Long Run: 
		   a Comparison on QAP Tasks
        author:	Roberto Battiti and Giampietro Tecchiolli
	number:  UTM 427 Febbraio 1994
	note:	11 pages, to appear in: 
			Computer and Mathematics with Applications
 	abstract:  
 Simulated Annealing (SA) and Tabu Search (TS) are compared on the Quadratic 
 Assignment Problem. A recent work on the same benchmark suite argued that SA 
 could achieve a reasonable solution quality with fewer function evaluations 
 than TS. The discussion is extended by showing that the conclusions must be 
 changed if the task is hard or a very good approximation of the optimal 
 solution is desired, or if CPU time is the relevant parameter. In addition, 
 a recently proposed version of TS (the Reactive Tabu Search) solves the 
 problem of finding the proper list size with an automatic memory-based 
 reaction mechanism.
	FTP-host: volterra.science.unitn.it
        FTP-file: pub/neuronit/rts-versus-sa.ps.Z
________________________________________________________________________________
 ARCHIVE-NN-6
        title: 	The continuous reactive tabu search: blending combinatorial 
                   optimization and stochastic search for global optimization
        author:	Roberto Battiti and Giampietro Tecchiolli
	number:  UTM 432 Maggio 1994
	note:	28 pages
 	abstract:  
A novel algorithm for the global optimization of functions (C-RTS) is 
presented, in which a combinatorial optimization method cooperates with a 
stochastic local minimizer.  The combinatorial optimization component, based
on the Reactive Tabu Search recently proposed by the authors, locates the most 
promising  ``boxes,'' where starting points for the local minimizer are 
generated. In order to cover a wide spectrum of possible applications with no 
user intervention, the method is designed with adaptive mechanisms:  the box 
size is adapted to the local structure of the function to be optimized, the 
search parameters are adapted to obtain a proper balance of diversification 
and intensification.  The algorithm is compared  with some existing algorithms,
 and the experimental results are presented for a suite of benchmark tasks.
	FTP-host: volterra.science.unitn.it
	FTP-file: pub/neuronit/crts.ps.Z
________________________________________________________________________________


More information about the Connectionists mailing list