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Evolutionary Computational Inteliigence. Lecture 6b: Towards Parameter Control. Ferrante Neri University of Jyväskylä. Motivation 1. An EA has many strategy parameters, e.g. mutation operator and mutation rate crossover operator and crossover rate

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evolutionary computational inteliigence

Evolutionary Computational Inteliigence

Lecture 6b: Towards Parameter Control

Ferrante Neri

University of Jyväskylä

motivation 1
Motivation 1

An EA has many strategy parameters, e.g.

  • mutation operator and mutation rate
  • crossover operator and crossover rate
  • selection mechanism and selective pressure (e.g. tournament size)
  • population size

Good parameter values facilitate good performance

Q1 How to find good parameter values ?

motivation 2
Motivation 2

EA parameters are rigid (constant during a run)


an EA is a dynamic, adaptive process


optimal parameter values may vary during a run

Q2: How to vary parameter values?

parameter tuning
Parameter tuning

Parameter tuning: the traditional way of testing and

comparing different values before the “real” run


  • users mistakes in settings can be sources of errors or sub-optimal performance
  • costs much time
  • parameters interact: exhaustive search is not practicable
  • good values may become bad during the run (e.g. Population size)
parameter setting problems
Parameter Setting: Problems
  • A wrong parameter setting can lead to an undesirable algorithmic behavious since it can lead to stagnation or premature convergence
  • Too large population size, stagnation
  • Too small population size, premature convergence
  • In some “moments” of the evolution I would like to have a large pop size (when I need to explore and prevent premature convergence); in other “moments” I would like to have a small one (when I need to exploit available genotypes)
parameter control
Parameter control

Parameter control: setting values on-line, during the

actual run, I would like that the algorithm “decides” by itself how to properly vary parameter setting over the run

Some popular options for pursuing this aim are:

  • predetermined time-varying schedule p = p(t)
  • using feedback from the search process
  • encoding parameters in chromosomes and rely on natural selection (similar to ES self-adaptation)
related problems
Related Problems


  • finding optimal p is hard, finding optimal p(t) is harder
  • still user-defined feedback mechanism, how to ”optimize”?
  • when would natural selection work for strategy parameters?

Provisional answer:

  • In agreement with the No Free Lunch Theorem, optimal control strategy does not exist. Nevertheless, there are a plenty of interesting proposals that can be very performing in some problems. Some of these strategies are very problem oriented while some others are much more robust and thus applicable in a fairly wide spectrum of optimization problems

Three major types of parameter control:

  • deterministic: some rule modifies strategy parameter without feedback from the search (based on some counter)
  • adaptive: feedback rule based on some measure monitoring search progress
  • self-adaptative: parameter values evolve along with solutions; encoded onto chromosomes they undergo variation and selection
evidence informing the change
Evidence informing the change

The parameter changes may be based on:

  • time or nr. of evaluations (deterministic control)
  • population statistics (adaptive control)
    • progress made
    • population diversity
    • gene distribution, etc.
  • relative fitness of individuals creeated with given values (adaptive or self-adaptive control)
evidence informing the change1
Evidence informing the change
  • Absolute evidence: predefined event triggers change, e.g. increase pm by 10% if population diversity falls under threshold x
  • Direction and magnitude of change is fixed
  • Relative evidence: compare values through solutions created with them, e.g. increase pm if top quality offspring came by high mut. Rates
  • Direction and magnitude of change is not fixed
deterministic control
Deterministic Control
  • It is based on a priori designed scheme which takes into account the variable time
  • The general idea is that the algorithm at the beginning of the process has different needs compared to the end of it
  • The main disadvantage of such an approach is that the algorithmic designer is supposed to know beforehand when the changes in parameter setting must be carried out
deterministic control1
Deterministic Control
  • 1st example: the population is enlarged at the end of the optimization process (after a prearranged number of generations)
  • 2nd example (Arabas 1994): assign to each solution a lifetime parameter. Fitter individuals must survive longer. The population is thus variable over a certain number of generations
adaptive control
Adaptive Control
  • It is online measured an indexto perform the parameter setting of the subsequent generations
  • Fitness improvements: if the algorithmic response in terms of improvements are relevant, then a parameter is unchanged. When the parameter setting is inefficient then the parameter setting is modified (e.g. mutation rate increased when no improvements are found)
adaptive control1
Adaptive Control
  • Fitness diversity: in high diversity conditions the algorithm needs to exploit available genotypes, in low diversity conditions the algorithm needs to detect new genotypes and search directions
  • A measurement of the diversity can be employed for enlarging population size in lo diversity condition and shrinking in high diversity conditions (analogously for mutation rate)
adaptive control2
Adaptive Control
  • 1st example:
adaptive control3
Adaptive Control
  • 2nd example
adaptive control4
Adaptive Control
  • 3rd example:
self adaptive control
Self-adaptive control
  • It encodes the control parameter within the genotype of the solution and it is based on the idea that it should evolve with the population
self adaptive control1
Self-adaptive control
  • If for example the control parameter measures the improvements due to the mutation, it can affect the mutation rate
  • Another popular option is to employ such a parameter for deciding the replication in the mating pool of the individuals (if the solution looks very promising) in order to have a tailored selection pressure
evaluation summary
Evaluation / Summary
  • Parameter control offers the possibility to use appropriate values in various stages of the search
  • Adaptive and self-adaptive parameter control
    • offer users “liberation” from parameter tuning
    • delegate parameter setting task to the evolutionary process
    • the latter implies a double task for an EA: problem solving + self-calibrating (overhead)
  • Robustness, insensivity of EA for variations assumed
    • If no. of parameters is increased by using (self)adaptation
    • For the “meta-parameters” introduced in methods