Genetic Algorithm classes

Maintainer

I M Ikram

Description

Some simple » genetic algorithm (GA) classes, aimed at solving optimization problems (both minimization and maximization).

Categories

• Libraries - Simulation

Versions

• (1996-05-20)

Links

• » Downloads (Old Eiffel Forum archive)

Details

Files

ran.e

interface to 'C' standard random number generation functions. The necessary external code is included in the file.

individual.e

individual_copyable.e: deferred classes relating to bit-string individuals

population.e, ss_population.e, gen_population.e

deferred classes relating to populations of bit strings

one_max.e, two_max.e, many_max.e, dejong_f1.e

some actual implementations of individuals corresponding to simple fitness functions

ss_pop_1.e, ss_pop_2.e, ss_pop_3.e, gen_pop_1.e

some actual implementations of populations capable of running simple genetic algorithms to solve optimization problems

ga_demo.e

allows any combination of the above fitness functions and genetic algorithms to be run

Class Hierarchy

RAN -- random numbers

INDIVIDUAL -- bit-string individuals         INDIVIDUAL_COPYABLE         -- reproducible individuals                 ONE_MAX, TWO_MAX, MANY_MAX, DEJONG_F1                 -- individuals meant to encode solutions to some simple optimization problems

POPULATION -- populations of bit-string individuals         SS_POPULATION         -- populations running steady-state algorithms                 SS_POP_1, SS_POP_2, SS_POP_3                 -- various steady-state strategies         GEN_POPULATION         -- populations running generational algorithms                 GEN_POP_1                 -- simple generational strategies

GA_DEMO -- demo/testing

Notes

• To define a new optimization problem, write a subclass of INDIVIDUAL_COPYABLE with a routine called evaluate, which should set the value of the individual to some problem-dependent function of the individual's bit-string. See example classes at the bottom of the INDIVIDUAL hierarchy.
• To define a new genetic algorithm, subclass either SS_POPULATION or GEN_POPULATION, depending on whether it is to be a steady-state or a generational algorithm. The new class should have an evolve routine that implements one evolutionary step or generation. This routine should select parents from the current population for mating, and then evaluate the resulting children and place them (if needed) in the population. See example classes at the bottom of the POPULATION hierarchy.
• New subclasses may be tested by adding relevant clauses to the inspect instructions in GA_DEMO's make_population routine.

Supported compilers

• SmartEiffel (SmallEiffel -0.98)

Licensing

• Copyrighted freeware

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