fine local tuning

7/28/2019 Fine Local Tuning

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FINE LOCAL TUNINGPREPARED BY: GEK CAGATAN


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FINE LOCAL TUNING

Problems in LOCAL SEARCHING arises when:

The DOMAINS of parameters are UNLIMITED

The NUMBER of parameters are quite LARGE

HIGH PRECISION is REQUIRED

The performance of GAs is quite poor


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FINE LOCAL TUNING

To IMPROVE Fine Local Tuning capabilities of GAs for

PRECISION problems,

We use SPECIAL MUTATION OPERATOR (with perform

quite different from traditional*)

TRADITIONAL

only one chromosome is changed at a time

uses only local knowledge- only the bit undergoing mutation is known

A B C D E F G H


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TEST CASES

The task of designing & implementing algorithms for the solution

CONTROL PROBLEMS is a DIFFICULT ONE

Algorithm breaks down on problems of MODERATE SIZE & COMPLEXITY

Difficult to deal with numerically

DYNAMIC OPTIMIZATION SPECIFIC METHODS (DOSM) can be used. If

even be more difficult for a layman to handle.


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OPTIMAL CONTROL PROBLEMS

GENETIC ALGORITHMS are used in this application. GAs are modified to enhance its performance.

We show the quality & applicability of the developed system by a comparative study

dynamic optimization problems

There are 3 simple Discrete-Time Optimal Control Models (these are frequently use

applications of optimal control):

LINEAR-QUADRATIC PROBLEM

HARVEST PROBLEM

PUSH CART PROBLEM

General Algebraic Modelling System (GAMS) with MINOS Optimizer- a standard computati

used for solving such problems.


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LINEAR- QUADRATIC PROBLEM


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HARVEST PROBLEM


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PUSH CART PROBLEM


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THE REPRESENTATIONS

In floating point representation each chromosome vector is coded

of floating point numbers of the same length as the solution vecto

The PRECISION depends on the underlying MACHINE (but gene

better than that of the binary representation).

We can EXTEND the precision of binary representation but it will SLOW DO

algorithm.

FLOATING POINT representation is capable of representing quite LARGE D


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THE SPECIALIZED OPERATORS

MUTATION and the CROSSOVER group


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MUTATION GROUP


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CROSSOVER GROUP


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EXPERIMENTS & RESULTS

We present the results of the evolution program for the optimal control problem

POP. SIZE= 70 (FIXED)

RUNS were made for 40,000 generations

LINEAR- QUADRATICPROBLEM


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EXPERIMENTS & RESULTS

HARVEST PROBLEM

PUSH CART PROBLEM


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EVOLUTION PROGRAM VERSUS OTHER METHODS


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PUSH CART PROBLEM


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THE SIGNIFICANCE OF NON-UNIFORM MUTATION


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THE SIGNIFICANCE OF NON-UNIFORM MUTATION


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CONCLUSION

The NON-UNIFORM mutation improved the fine local tuning ca

of the GA.


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CONCLUSION

The NUMERICAL RESULTS were compared with those obtained from a searccomputational package (GAMS).

While the Evolution Program gave us results comparable with the analytic solu

problems, GAMS failed for one of them.

The developed evolution program displayed some qualities not always presen

systems:

Optimization factor for the evolution program need not be continuous.

Optimization packages are all-or-nothing propositions.

Evolution programs give the users additional flexibility.

The user can specify the computation time.

The improvement of the performance of the system is often difficult for other optimiz

fine local tuning

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