Set Representation Using Schemata and its Constructing Method from Population in GA | Zendy

Naohiko HANAJIMA | Zendy; Mitsuhisa YAMASHITA | Zendy; Hiromitsu Hikita | Zendy

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Set Representation Using Schemata and its Constructing Method from Population in GA

Author(s) -

Naohiko HANAJIMA,

Mitsuhisa YAMASHITA,

Hiromitsu Hikita

Publication year - 1998

Publication title -

journal of robotics and mechatronics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.257

H-Index - 19

eISSN - 1883-8049

pISSN - 0915-3942

DOI - 10.20965/jrm.1998.p0315

Subject(s) - schema (genetic algorithms) , computer science , construct (python library) , set (abstract data type) , pareto principle , theoretical computer science , pareto optimal , simple (philosophy) , vector space , representation (politics) , population , artificial intelligence , mathematical optimization , mathematics , machine learning , programming language , pure mathematics , demography , sociology , politics , political science , law , philosophy , epistemology

When we invoke genetic algorithms (GAs), we retrieve enormous numbers of individuals. If we can construct, simply, a set having some sense from individuals, it would make engineering applications easier. Schemata in GAs is a simple forms representing such a set. We define modified schemata where instances of a schema represent a continuous region assuming that the GA phenotype is real vector space. We induce expected and maximum numbers of schemata required to represent any continuous region. We show ways to construct a schema set from individuals in GA, constructing a Pareto optimum set on multiobjective optimization theory.

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