/* -*- tab-width: 4 -*-
 *
 * Electric(tm) VLSI Design System
 *
 * File: PlacementGenetic.java
 * Written by Team 4: Benedikt Mueller, Richard Fallert
 * 
 * This code has been developed at the Karlsruhe Institute of Technology (KIT), Germany, 
 * as part of the course "Multicore Programming in Practice: Tools, Models, and Languages".
 * Contact instructor: Dr. Victor Pankratius (pankratius@ipd.uka.de)
 *
 * Copyright (c) 2010 Sun Microsystems and Static Free Software
 *
 * Electric(tm) is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Electric(tm) is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with Electric(tm); see the file COPYING.  If not, write to
 * the Free Software Foundation, Inc., 59 Temple Place, Suite 330,
 * Boston, Mass 02111-1307, USA.
 */
package com.sun.electric.tool.placement.genetic2;

import com.sun.electric.tool.placement.PlacementFrame;

import java.util.List;

/**
 * Combination of Genetic Algorithm and Simulated Annealing.
 * 
 * We have implemented a parallel genetic algorithm using a UnifiedPopulation
 * and multiple Evolver threads working on it.
 * 
 * We save only changes in our DeltaIndividuals, which saves CPU time and memory.
 * 
 * Please note: We had to make some methods inside PlacementFrame public:
 * PlacementPort.getPlacementNode()
 * PlacementPort.getRotatedOffX()
 * PlacementPort.getRotatedOffY()
 * PlacementNode.getPlacementX()
 * PlacementNode.getPlacementY()
 * @see GeneticPlacer
 * @see UnifiedPopulation
 * @see DeltaIndividual
 * @see Evolver
 * @see SimulatedAnnealing
 */
public class PlacementGenetic extends PlacementFrame
{
	GeneticPlacer placer = null;
	
	// maximum runtime of the placement algorithm in seconds
	public int maxRuntime = 240;
	// number of threads
	public int numThreads = 4;
	// if false: NO system.out.println statements
	public boolean printDebugInformation = true;
	
	// information for the benchmark framework
	String teamName = "team 4";
	String studentName1 = "Benedikt Mueller";
	String studentName2 = "Richard Fallert";
	String algorithmType = "genetic";
	
	public void setBenchmarkValues(int runtime, int threads, boolean debug) {
		maxRuntime = runtime;
		numThreads = threads;
		printDebugInformation = debug;
	}
	
	public String getAlgorithmName() { return "Genetic-2"; }
	
	public UnifiedPopulation getPopulation()
	{
		if(placer != null) return placer.getPopulation();
		else return null;
	}
	
	public PlacementGenetic()
	{	
	}
	
	/**
	 * Method to run the genetic algorithm to find a good placement.
	 * @param nodesToPlace a list of all nodes that are to be placed.
	 * @param allNetworks a list of all networks that connect the nodes.
	 * @param cellName the name of the cell being placed.
	 */
	public void runPlacement(List<PlacementNode> nodesToPlace, List<PlacementNetwork> allNetworks, String cellName)
	{
		placer = new GeneticPlacer(nodesToPlace, allNetworks, maxRuntime, numThreads, printDebugInformation);
		placer.runPlacement(nodesToPlace, allNetworks);
	}	
}