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diff --git a/nlpsolver/src/com/sun/star/comp/Calc/NLPSolver/BaseEvolutionarySolver.java b/nlpsolver/src/com/sun/star/comp/Calc/NLPSolver/BaseEvolutionarySolver.java
new file mode 100644
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+++ b/nlpsolver/src/com/sun/star/comp/Calc/NLPSolver/BaseEvolutionarySolver.java
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+/*************************************************************************
+ *
+ * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
+ *
+ * Copyright 2009 by Sun Microsystems, Inc.
+ *
+ * OpenOffice.org - a multi-platform office productivity suite
+ *
+ * This file is part of OpenOffice.org.
+ *
+ * OpenOffice.org is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU Lesser General Public License version 3
+ * only, as published by the Free Software Foundation.
+ *
+ * OpenOffice.org 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 Lesser General Public License version 3 for more details
+ * (a copy is included in the LICENSE file that accompanied this code).
+ *
+ * You should have received a copy of the GNU Lesser General Public License
+ * version 3 along with OpenOffice.org.  If not, see
+ * <http://www.openoffice.org/license.html>
+ * for a copy of the LGPLv3 License.
+ *
+ ************************************************************************/
+
+package com.sun.star.comp.Calc.NLPSolver;
+
+import com.sun.star.comp.Calc.NLPSolver.dialogs.DummyEvolutionarySolverStatusDialog;
+import com.sun.star.comp.Calc.NLPSolver.dialogs.EvolutionarySolverStatusUno;
+import com.sun.star.comp.Calc.NLPSolver.dialogs.IEvolutionarySolverStatusDialog;
+import com.sun.star.sheet.SolverConstraintOperator;
+import com.sun.star.uno.XComponentContext;
+import java.util.ArrayList;
+import net.adaptivebox.global.BasicBound;
+import net.adaptivebox.global.RandomGenerator;
+import net.adaptivebox.goodness.ACRComparator;
+import net.adaptivebox.goodness.BCHComparator;
+import net.adaptivebox.goodness.IGoodnessCompareEngine;
+import net.adaptivebox.knowledge.Library;
+import net.adaptivebox.knowledge.SearchPoint;
+import net.adaptivebox.problem.ProblemEncoder;
+
+public abstract class BaseEvolutionarySolver extends BaseNLPSolver {
+
+    public BaseEvolutionarySolver(XComponentContext xContext, String name) {
+        super(xContext, name);
+
+        registerProperty(m_swarmSize);
+        registerProperty(m_learningCycles);
+        registerProperty(m_guessVariableRange);
+        registerProperty(m_variableRangeThreshold);
+        registerProperty(m_useACRComperator);
+
+        registerProperty(m_useRandomStartingPoint);
+        registerProperty(m_useStrongerPRNG);
+
+        registerProperty(m_required);
+        registerProperty(m_tolerance);
+
+        registerProperty(m_enhancedSolverStatus);
+    }
+
+    private static class Variable {
+        private final CellMap CellMap;
+        private final int OriginalVariable;
+        private double MinValue;
+        private double MaxValue;
+        private double Granularity;
+
+        private Variable(CellMap cellMap, int originalVariable) {
+            this.CellMap = cellMap;
+            this.OriginalVariable = originalVariable;
+            this.MinValue = BasicBound.MINDOUBLE;
+            this.MaxValue = BasicBound.MAXDOUBLE;
+            this.Granularity = 0.0;
+        }
+    }
+
+    private class CalcProblemEncoder extends ProblemEncoder {
+
+        private final ArrayList<Variable> m_variables;
+        private final ArrayList<ExtSolverConstraint> m_constraints;
+
+        private CalcProblemEncoder(ArrayList<Variable> variables,
+                ArrayList<ExtSolverConstraint> constraints) throws Exception {
+            //m_variableCount variables to solve, target function + constraints to match
+            super(variables.size(), 1 + constraints.size());
+
+            m_variables = variables;
+            m_constraints = constraints;
+
+            double objective = m_maximize ? BasicBound.MAXDOUBLE : BasicBound.MINDOUBLE;
+            setDefaultYAt(0, objective, objective);
+
+            for (int i = 0; i < constraints.size(); i++) {
+                ExtSolverConstraint constraint = constraints.get(i);
+
+                switch (constraint.Operator.getValue()) {
+                    case SolverConstraintOperator.EQUAL_value:
+                        setDefaultYAt(i + 1, constraint.Data, constraint.Data);
+                        break;
+                    case SolverConstraintOperator.GREATER_EQUAL_value:
+                        setDefaultYAt(i + 1, constraint.Data, BasicBound.MAXDOUBLE);
+                        break;
+                    case SolverConstraintOperator.LESS_EQUAL_value:
+                        setDefaultYAt(i + 1, BasicBound.MINDOUBLE, constraint.Data);
+                        break;
+                    case SolverConstraintOperator.INTEGER_value:
+                        setDefaultYAt(i + 1, BasicBound.MINDOUBLE, BasicBound.MAXDOUBLE);
+                        break;
+                    case SolverConstraintOperator.BINARY_value:
+                        setDefaultYAt(i + 1, 0, 1);
+                        break;
+                }
+            }
+
+            for (int i = 0; i < m_variables.size(); i++) {
+                Variable variable = m_variables.get(i);
+                setDefaultXAt(i, variable.MinValue, variable.MaxValue, variable.Granularity);
+            }
+        }
+
+        @Override
+        protected double calcTargetAt(int index, double[] VX) {
+            if (index == 0) {
+                //calcTargetAt is called in a loop over all functions, so it's
+                //enough to set the variables in the first step only
+                for (int i = 0; i < m_variables.size(); i++) {
+                    CellMap variableMap = m_variables.get(i).CellMap;
+                    m_variableData[variableMap.Range][variableMap.Row][variableMap.Col] = VX[i];
+                }
+                for (int i = 0; i < m_cellRangeCount; i++)
+                    m_cellRangeData[i].setData(m_variableData[i]);
+
+                //errors are punished
+                if (m_objectiveCell.getError() != 0)
+                    return m_maximize ? BasicBound.MINDOUBLE : BasicBound.MAXDOUBLE;
+
+                double result = m_objectiveCell.getValue();
+
+                if (result >= m_toleratedMin && result <= m_toleratedMax && checkConstraints())
+                    m_toleratedCount++;
+
+                return result;
+            } else
+                return m_constraints.get(index - 1).getLeftValue();
+        }
+
+    }
+
+    protected CalcProblemEncoder m_problemEncoder;
+    protected Library m_library;
+    protected IGoodnessCompareEngine m_envCompareEngine;
+    protected IGoodnessCompareEngine m_specCompareEngine;
+    protected SearchPoint m_totalBestPoint;
+
+    protected int m_toleratedCount;
+    protected double m_toleratedMin;
+    protected double m_toleratedMax;
+
+    private final ArrayList<Variable> m_variables = new ArrayList<Variable>();
+
+    //properties
+    protected PropertyInfo<Integer> m_swarmSize = new PropertyInfo<Integer>("SwarmSize", 70, "Size of Swarm");
+    protected PropertyInfo<Integer> m_librarySize = new PropertyInfo<Integer>("LibrarySize", 210, "Size of Library");
+    protected PropertyInfo<Integer> m_learningCycles = new PropertyInfo<Integer>("LearningCycles", 2000, "Learning Cycles");
+    private final PropertyInfo<Boolean> m_guessVariableRange = new PropertyInfo<Boolean>("GuessVariableRange", true, "Variable Bounds Guessing");
+    private final PropertyInfo<Double> m_variableRangeThreshold = new PropertyInfo<Double>("VariableRangeThreshold", 3.0, "Variable Bounds Threshold (when guessing)"); //to approximate the variable bounds
+    private final PropertyInfo<Boolean> m_useACRComperator = new PropertyInfo<Boolean>("UseACRComparator", false, "Use ACR Comparator (instead of BCH)");
+    private final PropertyInfo<Boolean> m_useRandomStartingPoint = new PropertyInfo<Boolean>("UseRandomStartingPoint", false, "Use Random starting point");
+    private final PropertyInfo<Boolean> m_useStrongerPRNG = new PropertyInfo<Boolean>("UseStrongerPRNG", false, "Use a stronger random generator (slower)");
+    protected PropertyInfo<Integer> m_required = new PropertyInfo<Integer>("StagnationLimit", 70, "Stagnation Limit");
+    protected PropertyInfo<Double> m_tolerance = new PropertyInfo<Double>("Tolerance", 1e-6, "Stagnation Tolerance");
+    private final PropertyInfo<Boolean> m_enhancedSolverStatus = new PropertyInfo<Boolean>("EnhancedSolverStatus", true, "Show enhanced solver status");
+
+    protected IEvolutionarySolverStatusDialog m_solverStatusDialog;
+
+    private void prepareVariables(double[][] variableBounds) {
+        m_variables.clear();
+        for (int i = 0; i < m_variableCount; i++) {
+            Variable var = new Variable(m_variableMap[i], i);
+            var.MinValue = variableBounds[i][0];
+            var.MaxValue = variableBounds[i][1];
+            var.Granularity = variableBounds[i][2];
+            m_variables.add(var);
+        }
+    }
+
+    @Override
+    protected void initializeSolve() {
+        super.initializeSolve();
+
+        if (m_variableCount == 0)
+        {
+            return;
+        }
+        if (m_enhancedSolverStatus.getValue())
+            m_solverStatusDialog = new EvolutionarySolverStatusUno(m_xContext);
+        else
+            m_solverStatusDialog = new DummyEvolutionarySolverStatusDialog();
+
+        //Init:
+        double[][] variableBounds = new double[m_variableCount][3];
+        //approximate variable bounds
+        for (int i = 0; i < m_variableCount; i++) {
+            if (m_guessVariableRange.getValue()) {
+                double value = m_variableCells[i].getValue();
+
+                //0 is a bad starting point, so just pick some other.
+                //That is certainly not optimal but the user should specify
+                //bounds or at least a good starting point anyway.
+                if (value == 0.0)
+                    value = 1000;
+
+                double b1;
+                double b2;
+
+                if (m_assumeNonNegative.getValue()) {
+                    b1 = 0;
+                    b2 = value + value * 2 * m_variableRangeThreshold.getValue();
+                } else {
+                    b1 = value + value * m_variableRangeThreshold.getValue();
+                    b2 = value - value * m_variableRangeThreshold.getValue();
+                }
+
+                variableBounds[i][0] = Math.min(b1, b2);
+                variableBounds[i][1] = Math.max(b1, b2);
+            } else {
+                //that almost always leads to bad or no solutions at all
+                if (m_assumeNonNegative.getValue())
+                    variableBounds[i][0] = 0.0;
+                else
+                    variableBounds[i][0] = BasicBound.MINDOUBLE;
+                variableBounds[i][1] = BasicBound.MAXDOUBLE;
+            }
+            variableBounds[i][2] = 0.0;
+        }
+
+        //prepare constraints and parse them for variable bounds
+        ArrayList<ExtSolverConstraint> constraints = new ArrayList<ExtSolverConstraint>();
+        for (int i = 0; i < m_constraintCount; i++) {
+            Double doubleValue;
+
+            if (m_extConstraints[i].Right != null)
+                doubleValue = null;
+            else
+                doubleValue = m_extConstraints[i].Data;
+
+            boolean isVariableBound = false;
+            //If it refers to a cell, it has to be treated as constraint, not as
+            //bound.
+            if (m_extConstraints[i].Right == null) {
+                for (int j = 0; j < m_variableCount && !isVariableBound; j++) {
+                    if (m_constraints[i].Left.Sheet == super.m_variables[j].Sheet &&
+                            m_constraints[i].Left.Column == super.m_variables[j].Column &&
+                            m_constraints[i].Left.Row == super.m_variables[j].Row) {
+                        isVariableBound = true;
+
+                        //Therefore we try to use it as bounds for this variable.
+
+                        switch (m_extConstraints[i].Operator.getValue()) {
+                            case SolverConstraintOperator.EQUAL_value:
+                                if (doubleValue == null)
+                                    continue;
+                                variableBounds[j][0] = doubleValue;
+                                variableBounds[j][1] = doubleValue;
+                                break;
+                            case SolverConstraintOperator.GREATER_EQUAL_value:
+                                if (doubleValue == null)
+                                    continue;
+                                variableBounds[j][0] = doubleValue;
+                                break;
+                            case SolverConstraintOperator.LESS_EQUAL_value:
+                                if (doubleValue == null)
+                                    continue;
+                                variableBounds[j][1] = doubleValue;
+                                break;
+                            case SolverConstraintOperator.INTEGER_value:
+                                variableBounds[j][2] = 1.0;
+                                break;
+                            case SolverConstraintOperator.BINARY_value:
+                                variableBounds[j][0] = 0.0;
+                                variableBounds[j][1] = 1.0;
+                                variableBounds[j][2] = 1.0;
+                                break;
+                            default:
+                                //If it is neither <=, nor =, nor >=, we treat
+                                //it as normal constraint.
+                                isVariableBound = false;
+                        }
+                    }
+                }
+            }
+
+            if (!isVariableBound) {
+                constraints.add(m_extConstraints[i]);
+            }
+        }
+
+        prepareVariables(variableBounds);
+
+        try {
+            m_problemEncoder = new CalcProblemEncoder(m_variables, constraints);
+        } catch (Exception e) {
+            m_problemEncoder = null;
+            return;
+        }
+
+        m_library = new Library(m_librarySize.getValue(), m_problemEncoder);
+
+        if (m_useRandomStartingPoint.getValue()) {
+            m_totalBestPoint = m_problemEncoder.getEncodedSearchPoint();
+        } else {
+            m_totalBestPoint = m_problemEncoder.getFreshSearchPoint();
+            double[] currentValues = new double[m_variables.size()];
+            for (int i = 0; i < m_variables.size(); i++)
+                currentValues[i] = m_currentParameters[m_variables.get(i).OriginalVariable];
+            m_totalBestPoint.importLocation(currentValues);
+            m_problemEncoder.evaluate(m_totalBestPoint);
+        }
+        //input the chosen point into the library as reference for the individuals
+        m_library.getSelectedPoint(0).importPoint(m_totalBestPoint);
+
+        m_solverStatusDialog.setBestSolution(m_totalBestPoint.getObjectiveValue(), checkConstraints());
+
+        m_envCompareEngine = new BCHComparator();
+        m_specCompareEngine = m_useACRComperator.getValue() ? new ACRComparator(m_library, m_learningCycles.getValue()) : new BCHComparator();
+
+        RandomGenerator.useStrongerGenerator( m_useStrongerPRNG.getValue() );
+    }
+
+    protected void applySolution() {
+        double[] location = m_totalBestPoint.getLocation();
+
+        //make sure, the "Integer" variable type is met
+        m_problemEncoder.getDesignSpace().getMappingPoint(location);
+
+        //get the function value for our optimal point
+        for (int i = 0; i < m_variableCount; i++) {
+            m_variableCells[i].setValue(location[i]);
+            m_currentParameters[i] = location[i];
+        }
+        m_functionValue = m_objectiveCell.getValue();
+    }
+
+    @Override
+    protected void finalizeSolve() {
+        applySolution();
+
+        m_success = (m_objectiveCell.getError() == 0 && checkConstraints());
+
+        m_solverStatusDialog.setVisible(false);
+        m_solverStatusDialog.dispose();
+
+        super.finalizeSolve();
+    }
+
+    private boolean checkConstraints() {
+        boolean result = true;
+        for (int i = 0; i < m_constraintCount && result; i++) {
+            if (m_extConstraints[i].Left.getError() == 0) {
+                double value = m_extConstraints[i].getLeftValue();
+                double targetValue = m_extConstraints[i].Data;
+
+                switch (m_extConstraints[i].Operator.getValue()) {
+                    case SolverConstraintOperator.EQUAL_value:
+                        result = value == targetValue;
+                        break;
+                    case SolverConstraintOperator.GREATER_EQUAL_value:
+                        result = value >= targetValue;
+                        break;
+                    case SolverConstraintOperator.LESS_EQUAL_value:
+                        result = value <= targetValue;
+                        break;
+                    case SolverConstraintOperator.INTEGER_value:
+                        result = Math.rint(value) == value;
+                        break;
+                    case SolverConstraintOperator.BINARY_value:
+                        result = (value == 0.0 || value == 1.0);
+                        break;
+                }
+            } else {
+                result = false;
+            }
+        }
+
+        return result;
+    }
+
+}