Examples of SimulatedAnnealingSearch

aima.core.search.local.SimulatedAnnealingSearch
Artificial Intelligence A Modern Approach (3rd Edition): Figure 4.5, page 126.
```
 function SIMULATED-ANNEALING(problem, schedule) returns a solution state current <- MAKE-NODE(problem.INITIAL-STATE) for t = 1 to INFINITY do T <- schedule(t) if T = 0 then return current next <- a randomly selected successor of current /\E <- next.VALUE - current.value if /\E > 0 then current <- next else current <- next only with probability eˆ(/\E/T) 
```
Figure 4.5 The simulated annealing search algorithm, a version of stochastic hill climbing where some downhill moves are allowed. Downhill moves are accepted readily early in the annealing schedule and then less often as time goes on. The schedule input determines the value of the temperature T as a function of time. @author Ravi Mohan @author Mike Stampone

Examples of aima.core.search.local.SimulatedAnnealingSearch

    try {
      Problem problem = new Problem(new NQueensBoard(8),
          NQueensFunctionFactory.getIActionsFunction(),
          NQueensFunctionFactory.getResultFunction(),
          new NQueensGoalTest());
      SimulatedAnnealingSearch search = new SimulatedAnnealingSearch(
          new AttackingPairsHeuristic());
      SearchAgent agent = new SearchAgent(problem, search);


      System.out.println();
      printActions(agent.getActions());
      System.out.println("Search Outcome=" + search.getOutcome());
      System.out.println("Final State=\n" + search.getLastSearchState());
      printInstrumentation(agent.getInstrumentation());
    } catch (Exception e) {
      e.printStackTrace();
    }
  }

View Full Code Here

Examples of aima.core.search.local.SimulatedAnnealingSearch

    System.out.println("\nEightPuzzleDemo Simulated Annealing  Search -->");
    try {
      Problem problem = new Problem(random1, EightPuzzleFunctionFactory
          .getActionsFunction(), EightPuzzleFunctionFactory
          .getResultFunction(), new EightPuzzleGoalTest());
      SimulatedAnnealingSearch search = new SimulatedAnnealingSearch(
          new ManhattanHeuristicFunction());
      SearchAgent agent = new SearchAgent(problem, search);
      printActions(agent.getActions());
      System.out.println("Search Outcome=" + search.getOutcome());
      System.out.println("Final State=\n" + search.getLastSearchState());
      printInstrumentation(agent.getInstrumentation());
    } catch (Exception e) {
      e.printStackTrace();
    }
  }

View Full Code Here

Examples of aima.core.search.local.SimulatedAnnealingSearch


  @Test
  public void testForGivenNegativeDeltaEProbabilityOfAcceptanceDecreasesWithDecreasingTemperature() {
    // this isn't very nice. the object's state is uninitialized but is ok
    // for this test.
    SimulatedAnnealingSearch search = new SimulatedAnnealingSearch(null);
    int deltaE = -1;
    double higherTemperature = 30.0;
    double lowerTemperature = 29.5;


    Assert.assertTrue(search.probabilityOfAcceptance(lowerTemperature,
        deltaE) < search.probabilityOfAcceptance(higherTemperature,
        deltaE));
  }

View Full Code Here

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.