001 /*
002 * Java Genetic Algorithm Library (jenetics-3.0.0).
003 * Copyright (c) 2007-2014 Franz Wilhelmstötter
004 *
005 * Licensed under the Apache License, Version 2.0 (the "License");
006 * you may not use this file except in compliance with the License.
007 * You may obtain a copy of the License at
008 *
009 * http://www.apache.org/licenses/LICENSE-2.0
010 *
011 * Unless required by applicable law or agreed to in writing, software
012 * distributed under the License is distributed on an "AS IS" BASIS,
013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014 * See the License for the specific language governing permissions and
015 * limitations under the License.
016 *
017 * Author:
018 * Franz Wilhelmstötter (franz.wilhelmstoetter@gmx.at)
019 */
020 package org.jenetics;
021
022 import static java.lang.String.format;
023 import static org.jenetics.internal.util.Equality.eq;
024
025 import java.io.Serializable;
026 import java.util.function.Function;
027
028 import org.jenetics.internal.util.Equality;
029 import org.jenetics.internal.util.Hash;
030
031 /**
032 * Implements an exponential fitness scaling, whereby all fitness values are
033 * modified the following way.
034 * <p><img src="doc-files/exponential-scaler.gif"
035 * alt="f_s=\left(a\cdot f+b \rigth)^c"
036 * >.</p>
037 *
038 * @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
039 * @since 1.0
040 * @version 2.0 — <em>$Date: 2014-12-28 $</em>
041 */
042 public final class ExponentialScaler
043 implements
044 Function<Double, Double>,
045 Serializable
046 {
047 private static final long serialVersionUID = 2L;
048
049 public static final ExponentialScaler SQR_SCALER = new ExponentialScaler(2);
050 public static final ExponentialScaler SQRT_SCALER = new ExponentialScaler(0.5);
051
052 private final double _a;
053 private final double _b;
054 private final double _c;
055
056 /**
057 * Create a new FitnessScaler.
058 *
059 * @param a <pre>fitness = (<strong>a</strong> * fitness + b) ^ c</pre>
060 * @param b <pre>fitness = (a * fitness + <strong>b</strong>) ^ c</pre>
061 * @param c <pre>fitness = (a * fitness + b) ^ <strong>c</strong></pre>
062 */
063 public ExponentialScaler(final double a, final double b, final double c) {
064 _a = a;
065 _b = b;
066 _c = c;
067 }
068
069 /**
070 * Create a new FitnessScaler.
071 *
072 * @param b <pre>fitness = (1 * fitness + <strong>b</strong>) ^ c</pre>
073 * @param c <pre>fitness = (1 * fitness + b) ^ <strong>c</strong></pre>
074 */
075 public ExponentialScaler(final double b, final double c) {
076 this(1.0, b, c);
077 }
078
079 /**
080 * Create a new FitnessScaler.
081 *
082 * @param c <pre>fitness = (1 * fitness + 0) ^ <strong>c</strong></pre>
083 */
084 public ExponentialScaler(final double c) {
085 this(1.0, 0.0, c);
086 }
087
088
089 @Override
090 public Double apply(final Double value) {
091 return Math.pow((_a*value + _b), _c);
092 }
093
094 @Override
095 public int hashCode() {
096 return Hash.of(getClass())
097 .and(_a)
098 .and(_b)
099 .and(_c).value();
100 }
101
102 @Override
103 public boolean equals(final Object obj) {
104 return Equality.of(this, obj).test(selector ->
105 eq(_a, selector._a) &&
106 eq(_b, selector._b) &&
107 eq(_c, selector._c)
108 );
109 }
110
111 @Override
112 public String toString() {
113 return format(
114 "%s[a=%f, b=%f, c=%f]",
115 getClass().getSimpleName(), _a, _b, _c
116 );
117 }
118 }
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