A rating function determines the fitness of a given solution to the problem in question. They are generally used in order to guide problem solvers. However, rating functions can also be used in order to inject additional code into solvers. This can be seen as a form of dependency injection. A simple example is provided at the end of this article.
Note: During the design phase of the second version of Gel I searched for methods to create complex constraints out of relatively simple constraints. Typed ratings can be used in order to i.e. negate a given constraint with high quality. I also looked for ways to organize complex manipulations of solutions. However, typed ratings were of no use for the solvers during my master thesis. Instead, the given solution was queried for variables that dissatisfy certain aspects of the constraints.
Note:This article does currently not consider existing research yet.
The hill climber for discrete values is able to imitate any discrete solving algorithm that optimizes a given complete solution. For a given history of intermediate solutions one has to determine a path in the search space visiting all intermediate solutions in the given order. The rating function can be used in order to determine the next step of the hill climber at the current step of the solution path.
If there is a high number of neighbors at the current position of the problem space, then the performance of the hill climber might be inappropriate. This can be compensated by adjusting the problem space dynamically in order to minimize the number of neighbors during a hill climbing step. This shows that the solution space can also be used in order to inject code into solving programs.
The following Python code shows how Simulated Annealing can be injected into the hill climbing algorithm. Note that the implemented rating functions are based on side effects and are therefore not suitable for certain local search algorithms.
#!/usr/bin/env python3 """This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/.""" __author__ = 'Mārtiņš Avots' import unittest from random import uniform, shuffle from math import log, inf class annealing_relative_rating: def __init__(self, rating_function, temperature_function, rating_improvement_factor, rating_diminishment_factor): self.rating_function = rating_function self.temperature_function = temperature_function self.counter = 0 self.rating_improvement_factor = rating_improvement_factor self.rating_diminishment_factor = rating_diminishment_factor def __call__(self, solution): rating = self.rating_function(solution) self.counter += 1 temperature = self.temperature_function(self.counter) if rating == 0 or temperature == 0: return rating elif uniform(0, 1) >= temperature: return rating * self.rating_improvement_factor else: if rating * self.rating_diminishment_factor == 0: return rating else: return rating * self.rating_diminishment_factor class annealing_absolute_rating: def __init__(self, rating_function, temperature_function, max_rating): self.rating_function = rating_function self.temperature_function = temperature_function self.counter = 0 self.max_rating = max_rating def __call__(self, solution): rating = self.rating_function(solution) self.counter += 1 temperature = self.temperature_function(self.counter) if rating == 0 or temperature == 0: return rating elif uniform(0, 1) >= temperature: return rating else: return self.max_rating def climb_hill(rating_function, problem_space, current_solution): best_solution = current_solution for neighbour in problem_space(current_solution): if rating_function(best_solution) > rating_function(neighbour): best_solution = neighbour return best_solution def Queen_N_problem(n): return [0] * n def Queen_N_rating(queen_problem): rVal = 0 for i in range(len(queen_problem)): for j in range(len(queen_problem)): if [i] != [j] and queen_problem[i] == queen_problem[j]: rVal += 1 for i in range(0, len(queen_problem)  1): for j in range(i + 1, len(queen_problem)): k = j  i if queen_problem[i] == queen_problem[j]  k: rVal += 1 if queen_problem[i] == queen_problem[j] + k: rVal += 1 return rVal def Queen_N_neighbours(queen_problem): rVal = [] for i in range(len(queen_problem)): for j in range(len(queen_problem)): neighbour = list(queen_problem) neighbour[i] = j rVal.append(neighbour) shuffle(rVal) return rVal class HillClimbingTestLocalOptima(unittest.TestCase): """By default only functions starting with test are executed during testing.""" def __temperatureTestFunction(self, x): """Determined by trial and error from universal allocation program's temperature functions.""" return (log(4.0) / log((x + 1)**1.5))  0.05 def __hillClimbingTest_GetsStuckInBadLocalOptima(self, rating_function, max_iterations): for i in range(max_iterations): solution = Queen_N_problem(8) for i in range(1000): solution = climb_hill(rating_function, Queen_N_neighbours, solution) if Queen_N_rating(solution) == 0: break if Queen_N_rating(solution) != 0: return True return False def testClassicHillClimbingHasBadLocalOptima(self): self.assertTrue( self.__hillClimbingTest_GetsStuckInBadLocalOptima(Queen_N_rating, 10) ) def testAbsoluteAnnealingHillClimberFindsGlobalOptima(self): self.assertFalse( self.__hillClimbingTest_GetsStuckInBadLocalOptima( annealing_absolute_rating( Queen_N_rating, self.__temperatureTestFunction, inf), # A floatingpoint positive infinity. 10) ) def testRelativeAnnealingHillClimberFindsGlobalOptima(self): self.assertFalse( self.__hillClimbingTest_GetsStuckInBadLocalOptima( annealing_relative_rating( Queen_N_rating, self.__temperatureTestFunction, 1./8., # Determined by trial and error. 8), # Determined by trial and error. 10) ) if __name__ == "__main__": unittest.main()
Every rating function is of one of the following types:
The higher the rating the better the solution. Such a function is also called an objective function, a reward function, a object function, a utility function or a fitness function.

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The lower the rating the better the solution. A cost function is also called loss function.

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Consecutively, some properties related to the quality of rating functions are listed.
A proportional object function evaluates the compliance of a solution to a given problem: if the rating of a solution A is Xtimes higher as the rating of another solution B then B disagrees the given constraints Xtimes as much as A. Constraint weights are considered in this context.

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An exact cost function is a proportional cost function where an optimal solution is rated with zero. Note that a rating function that only returns the number of dissatisfied constraints may not be exact. An exact object function needs to respect the weights of the constraints in questions.

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An invertible objective function has a return value with a known upper bound. This means that one can take an invertible cost function and create a negation of that function. This is equal to a conversion of a fitness function into a loss function and vice versa.

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For a given distorted cost function: If the rating of a solution A is X times higher as the rating of another solution B then A might not disagree the given constraints Xtimes more than B. Constraint weights are considered in this context.

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If a given solution is rated multiple times with such a function, then the result is always the same.

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If a given solution is rated multiple times with such a function than the rating may not be the same: i.e. a learning program that learned a rating function but still adjusts its weightings. If a side effect free system is used, then this can be implemented via the concept of time. This can be done by adding a parameter to the rating function representing the current time:

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An alternative approach is to implement the rating function as a monad:

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A rating function where every solving algorithm has a low probability to find an optimal solution to a randomly chosen problem instance for a given problem:

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Subsequently, some types of object functions with different qualities are listed:
For a given solution and search space this cost function returns the minimum distance between the given solution and any optimal solution:

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For a given solution, feasible region and solver this cost function returns the expected minimal distance between the given solution and any optimal solution:

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For a given feasible region, solution, local search algorithm and a maximum search space walk distance this profit function returns the rating improvements that the search algorithm is expected to find for a given neighbor of the current solution:

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For a given feasible region, solution, local search algorithm and a maximum search space walk distance this fitness function returns the probability that the search algorithm will find an optimal solution:

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