doi:10.1016/S0378-4371(00)00396-4
Copyright © 2000 Elsevier Science B.V. All rights reserved.
Stochastic urn models of innovation and search dynamics
Werner Ebeling
,
, a, Lutz Molgedeya and Axel Reimanna
a Humboldt-University Berlin, Institute of Physics, Invalidenstrasse 110, D-10115 Berlin, Germany
Received 8 May 2000;
revised 13 June 2000.
Available online 4 December 2000.
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Abstract
This work is devoted to applications of the Ehrenfest urn model to innovation and search processes. In the first part we discuss systems of two urns serving as models of innovation processes. The elementary act of innovation is considered as a transition from old (technologies, way of production, behavior, decisions) to new. The survival probability of the new under the influence of stochastic effects is discussed. In the second part we study systems of s
1 urns serving as models for optimal solution searching in optimization problems. The problem is to find the minimum on a large set of real numbers Ui using a total of N seekers (N
2–100) simultaneously. The potential Ui is defined on the integer set i=1,…,s, where s is extremely large. In particular, we consider the frustrated periodic strings and the merit problem. The known equations for thermodynamic search processes and for simple models of biological evolution are unified by defining a two-parameter family of equations which embeds both cases. The search parameters are controlled by means of seeker ensemble dispersion.
Author Keywords: Urn model; Innovation; Search process; Variability; Parameter control
PACS classification codes: 02.50.Ey; 05.10.-a; 05.10.Gg
Fig. 1. Winning probability for two innovators in a committee consisting of N=5,10,15,20 members. Parameters: Em=1, Bm≡Bn=1.
Fig. 2. Boltzmann strategy: fitness of best seeker; 1000 run average; Engel sequence length L=15, tmax=1000.
Fig. 3. Boltzmann–Darwin strategy: fitness of best seeker, 1000 run average; 4 seekers tournament, Engel sequence length L=15, temp. T=1, time tmax=1000.
Fig. 4. Boltzmann strategy vs. controlled annealing; Engel sequence length L=15, time tmax=1000.
Fig. 5. Boltzmann–Darwin strategy: 4 seekers tournament, Engel sequence length L=15, N=20, temp. T=1; Hamming distance distribution: H(seeker, nearest global optimum); time tmax=104; right ordinate axis and solid line: ensemble fitness dispersion.
Fig. 6. Boltzmann–Darwin strategy: fitness of best seeker, Used parameters: 4 seekers tournament, Engel sequence length L=15, temperature T=1, time tmax=1000; Pmut=100% represents a random search process; mutation rate adaption by variability control yields almost always best results.
Fig. 7. Boltzmann strategy: mean ensemble fitness; used parameters: merit sequence length L=29, time tmax=10.000; solid line: normal mutation operator; dashed line: skew-symmetric mutation operator.
Fig. 8. Merit ensemble fitness, parameters: skew symmetric mutation operator, sequence length L=29, time tmax=1000; (—–): controlled annealing, initial temp. T=2; (- - - - -): adaptive Boltzmann–Darwin strategy, tournament of 4 seekers; T=0.3.