## Continue your professional development with Scribd

Exclusive 60 day trial to the world's largest digital library.

Join 1+ million members and get unlimited* access to books, audiobooks.

Cancel anytime.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- Jack rental-car-problem by Zahra Sadeghi 485 views
- Bluetooth Technoloty by Zahra Sadeghi 283 views
- Self Organization Map by Zahra Sadeghi 446 views
- Neural networks by Zahra Sadeghi 324 views
- Parametric and non parametric class... by Zahra Sadeghi 536 views
- A simplex nelder mead genetic algor... by Aboul Ella Hassanien 1069 views

Just added to SlideShare
##
Continue your professional development with Scribd

Exclusive 60 day trial to the world's largest digital library.

Join 1+ million members and get unlimited* access to books, audiobooks.

Cancel anytime.

penalty methods

No Downloads

Total views

1,822

On SlideShare

0

From Embeds

0

Number of Embeds

5

Shares

0

Downloads

46

Comments

0

Likes

5

No notes for slide

- 1. Penalty function methods Zahra Sadeghi
- 2. 2 Solution space
- 3. 3 A constrained optimization problem is usually written as a nonlinear optimization problem: x is the vector of solutions, F is the feasible region and S is the whole search space There are q inequality and m-q equality constraints f(x) is usually called the objective function or criterion function. Objective function and constraints could be linear or nonlinear in the problem. Vector x that satisfies all the constraints is a feasible solution of the problem. nonlinear programming problem is to find a point x* ∈ F such that F(x *) ≤f(x) for all x ∈F CONSTRAINED OPTIMIZATION
- 4. 4 Category 1: Methods based on penalty functions - Death Penalty - Static Penalties - Dynamic Penalties - Annealing Penalties - Adaptive Penalties - Segregated GA - Co-evolutionary Penalties Category 2: Methods based on a search of feasible solutions - Repairing unfeasible individuals - Superiority of feasible points - Behavioral memory Category 3: Methods based on preserving feasibility of solutions - The GENOCOP system - Searching the boundary of feasible region - Homomorphous mapping Category 4: Hybrid methods CONSTRAINT HANDLING in GAs
- 5. 5 Penalty Functions
- 6. 6 Death Penalty rejects unfeasible solutions from the population considering only the points that are in feasible when the problem is highly constrained, the algorithm will spend a lot of time to find too few feasible solutions. region of the search space prevents to find better solutions.
- 7. 7 to penalize infeasible solutions is to apply a constant penalty to those solutions which violate feasibility in any way. penalty parameters don’t depend on the current generation number and a constant penalty is applied to unfeasible solutions. The penalty function for a problem with m constraints fp(x) is the penalized objective function, f(x) is the unpenalized objective function, Ci is a constant imposed for violation of constraint i. Static Penalties
- 8. 8 Cnt’d… More common and more effective is to penalize according of distance to feasibility, assuming distance = number of constraints violated. A more sophisticated and more effective penalty includes a distance metric for each constraint, and adds a penalty which becomes more severe with distance from feasibility. distance metric , provides information concerning the nearness of the solution to feasibility, this nearness to feasibility is relevant in the same magnitude to the fitness of the solution. A general formulation is as follows for a minimization problem: di is the distance metric of constraint i applied to solution x k is user defined exponent, with values of k of 1 or 2 often used. Selection of Ci is more difficult.
- 9. 9 Homaifar, Lai, & Qi method Steps : Generate l levels of violation for each constraint, Generate penalty coefficient Rij (i=1,...,l ; j=1,…,m) for each level of violation and each constraint. The bigger coefficients are given to the bigger violation levels, Generate a random population using both feasible and unfeasible individuals, Evaluate individuals using Rij indicates the penalty coefficient corresponding to jth constraint and ith violation level. m is the number of constraints.
- 10. 10 Dynamic Penalties si(t) is a monotonically non-decreasing in value with t. Metrics for t include number of generations or the number of solutions searched. Recent uses of this approach include Joines and Houck
- 11. 11 penalty parameters are usually dependent on the current generation number. Joines and Houck
- 12. 12 Michalewicz and Attia : simulated annealing Steps: Separate all constraints into four subsets: linear equations, linear inequalities,nonlinear equations and nonlinear inequalities, starting point =a random single point that satisfies linear constraints. initial population = of copies of this single point Create a set of active constraints A = nonlinear equations and violated nonlinear inequalities, Set τ=τ0, Evolve the population, If τ<τf (freezing point) stop the procedure, otherwise, - Diminish τ, - Use the best solution as a starting point for next generation, - Update A, -Repeat the previous step of the main part. The algorithm is terminated at a previously determined freezing point τf . Michalewicz and Attia used τ0=1, τi+1=0.1τi , τf=0.000001 Annealing Penalties
- 13. 13 Adaptive Penalties not adaptive to the ongoing success of the search and cannot guide the search to particularly attractive regions or away from unattractive regions based on what has already been observed. Bean and Hadj-Alouane (1992) propose penalty functions which are revised based on the feasibility or infeasibility of the best, penalized solution during recent generations
- 14. 14 Smith and Tate (1993). •NFT is the threshold distance from the feasible region at which the user would consider the search as “getting warm.” • Unfeasible solutions at this distance are considered to be reasonable since they are very close to the feasible region. •The penalty function encourages the evolutionary algorithm to explore within the feasible region and the NFT-neighborhood of the feasible region, and discourage search beyond that threshold. Fall(t) denotes the unpenalized value of the best solution yet found, Ffeas(t) denotes the value of the best feasible solution yet found.
- 15. 15 Segregated GA set two penalty parameters (say, p1 and p2) in two different populations. aim : to overcome the problem of too high and too low penalties If a small value is selected for p1 and a large value for p2, a simultaneous convergence from feasible and unfeasible sides can be achieved. Steps: - Generate 2 x pop individuals at random (pop is the population size), - Design two different evaluation functions: - Evaluate each individual according to two evaluation functions, - Create two ranked lists according to f1(x) and f2 (x), - Combine two ranked lists in a single one ranked population with size pop, - Apply genetic operators, - Evaluate the new population using both penalty parameters, - From the old and new populations generate two populations of the size pop each, - Repeat the last four steps of the process.
- 16. 16 Thanks

No public clipboards found for this slide

Be the first to comment