Download Free Medical Genetic Algorithms-A Tutorial PowerPoint Presentation

Genetic Algorithms-A Tutorial PowerPoint Presentation

DOWNLOAD MEDICAL POWERPOINT PRESENTATION

About This Presentation

Description : Check out this medical presentation on Genetics, which is titled "Genetic Algorithms: A Tutorial", to know about directed search algorithms based on the mechanics of biological evolution.

Tags : Genetics, Genetic Algorithms, Tutorial, mechanics, biological evolution

Published on : Jul 12, 2021 | Views : 2504 | Downloads : 1

Shareable Url :

Embed Code :

Comment

Transcript

Slide 1-

“Genetic Algorithms are good at taking large, potentially huge search spaces and navigating them, looking for optimal combinations of things, solutions you might not otherwise find in a lifetime.” - Salvatore Mangano Computer Design, May 1995 Genetic Algorithms: A Tutorial
Slide 2-

The Genetic Algorithm Directed search algorithms based on the mechanics of biological evolution Developed by John Holland, University of Michigan (1970’s) To understand the adaptive processes of natural systems To design artificial systems software that retains the robustness of natural systems
Slide 3-

The Genetic Algorithm (cont.) Provide efficient, effective techniques for optimization and machine learning applications Widely-used today in business, scientific and engineering circles
Slide 4-

Classes of Search Techniques
Slide 5-

Components of a GA A problem to solve, and ... Encoding technique (gene, chromosome) Initialization procedure (creation) Evaluation function (environment) Selection of parents (reproduction) Genetic operators (mutation, recombination) Parameter settings (practice and art)
Slide 6-

Simple Genetic Algorithm { initialize population; evaluate population; while TerminationCriteriaNotSatisfied { select parents for reproduction; perform recombination and mutation; evaluate population; } }
Slide 7-

The GA Cycle of Reproduction reproduction population evaluation modification discard deleted members parents children modified children evaluated children
Slide 8-

Population Chromosomes could be: Bit strings (0101 ... 1100) Real numbers (43.2 -33.1 ... 0.0 89.2) Permutations of element (E11 E3 E7 ... E1 E15) Lists of rules (R1 R2 R3 ... R22 R23) Program elements (genetic programming) ... any data structure ... population
Slide 9-

Reproduction reproduction population parents children Parents are selected at random with selection chances biased in relation to chromosome evaluations.
Slide 10-

Chromosome Modification modification children Modifications are stochastically triggered Operator types are: Mutation Crossover (recombination) modified children
Slide 11-

Mutation: Local Modification Before: (1 0 1 1 0 1 1 0) After: (0 1 1 0 0 1 1 0) Before: (1.38 -69.4 326.44 0.1) After: (1.38 -67.5 326.44 0.1) Causes movement in the search space (local or global) Restores lost information to the population
Slide 12-

Crossover: Recombination P1 (0 1 1 0 1 0 0 0) (0 1 0 0 1 0 0 0) C1 P2 (1 1 0 1 1 0 1 0) (1 1 1 1 1 0 1 0) C2 Crossover is a critical feature of genetic algorithms: It greatly accelerates search early in evolution of a population It leads to effective combination of schemata (subsolutions on different chromosomes) *
Slide 13-

Evaluation The evaluator decodes a chromosome and assigns it a fitness measure The evaluator is the only link between a classical GA and the problem it is solving evaluation evaluated children modified children
Slide 14-

Deletion Generational GA: entire populations replaced with each iteration Steady-state GA: a few members replaced each generation population discard discarded members
Slide 15-

An Abstract Example Distribution of Individuals in Generation 0 Distribution of Individuals in Generation N
Slide 16-

A Simple Example “The Gene is by far the most sophisticated program around.” - Bill Gates, Business Week, June 27, 1994
Slide 17-

A Simple Example The Traveling Salesman Problem: Find a tour of a given set of cities so that each city is visited only once the total distance traveled is minimized
Slide 18-

Representation Representation is an ordered list of city numbers known as an order-based GA. 1) London 3) Dunedin 5) Beijing 7) Tokyo 2) Venice 4) Singapore 6) Phoenix 8) Victoria CityList1 (3 5 7 2 1 6 4 8) CityList2 (2 5 7 6 8 1 3 4)
Slide 19-

Crossover Crossover combines inversion and recombination: * * Parent1 (3 5 7 2 1 6 4 8) Parent2 (2 5 7 6 8 1 3 4) Child (2 5 7 2 1 6 3 4) This operator is called the Order1 crossover.
Slide 20-

Mutation involves reordering of the list: * * Before: (5 8 7 2 1 6 3 4) After: (5 8 6 2 1 7 3 4) Mutation
Slide 21-

TSP Example: 30 Cities
Slide 22-

Solution i (Distance = 941)
Slide 23-

Solution j(Distance = 800)
Slide 24-

Solution k(Distance = 652)
Slide 25-

Best Solution (Distance = 420)
Slide 26-

Overview of Performance
Slide 27-

Considering the GA Technology “Almost eight years ago ... people at Microsoft wrote a program [that] uses some genetic things for finding short code sequences. Windows 2.0 and 3.2, NT, and almost all Microsoft applications products have shipped with pieces of code created by that system.” - Nathan Myhrvold, Microsoft Advanced Technology Group, Wired, September 1995
Slide 28-

Issues for GA Practitioners Choosing basic implementation issues: representation population size, mutation rate, ... selection, deletion policies crossover, mutation operators Termination Criteria Performance, scalability Solution is only as good as the evaluation function (often hardest part)
Slide 29-

Benefits of Genetic Algorithms Concept is easy to understand Modular, separate from application Supports multi-objective optimization Good for “noisy” environments Always an answer; answer gets better with time Inherently parallel; easily distributed
Slide 30-

Benefits of Genetic Algorithms (cont.) Many ways to speed up and improve a GA-based application as knowledge about problem domain is gained Easy to exploit previous or alternate solutions Flexible building blocks for hybrid applications Substantial history and range of use
Slide 31-

When to Use a GA Alternate solutions are too slow or overly complicated Need an exploratory tool to examine new approaches Problem is similar to one that has already been successfully solved by using a GA Want to hybridize with an existing solution Benefits of the GA technology meet key problem requirements
Slide 32-

Some GA Application Types
Slide 33-

Conclusions Question: ‘If GAs are so smart, why ain’t they rich?’ Answer: ‘Genetic algorithms are rich - rich in application across a large and growing number of disciplines.’ - David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning