An updated survey of GA-based multiobjective optimization techniques

By Carlos Artemio Coello Coello. CSUR, 2000

858 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/43f132cbc24c433c16b5aabc4393e398d1775d81

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Christian Blum
2. Yaochu Jin
3. Qingfu Zhang
4. Xin-She Yang
5. Kalyanmoy Deb
6. Hui Li
7. Ran Cheng
8. Andrea Roli

In Computational Theory and Mathematics

1. Qingfu Zhang
2. Yaochu Jin
3. Kalyanmoy Deb
4. Thomas Weise
5. Hui Li
6. Christian Blum
7. David W. Coit
8. Ran Cheng

In Control and Systems Engineering

1. Christian Blum
2. Kenneth De Jong
3. Joshua D. Knowles
4. Mark S. Daskin
5. Seyed M. R. Iravani
6. Andrea Roli
7. Richard F. Hartl
8. Tomasz Arciszewski

Citations in the corpus

• Multi-objective optimization using genetic algorithms: A tutorial (2006, background): “Several survey papers [1-3, 12, 14, 22, 51, 54, 55] have been published on evolutionary”
• Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection (2004, background): “…by Scha er (1985), numerous related approaches have been published (see Fonseca and Fleming, 1993; Horn et al., 1994; Srinivas and Deb, 1994; Murata and Ishibuchi, 1995; Coello and Christiansen, 1998; Zitzler and Thiele, 1999; Hanne, 2000, for examples and Coello, 2000; Deb, 2001, for surveys).”
• MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition (2007, background): “There is no decomposition involved in the majority of the current state-of-the-art multiobjective evolutionary algorithms (MOEAs) [2]–[4], [12]–[19].”
• The orienteering problem with stochastic profits (2008, methodology): “For a discussion of the techniques for these purposes and a general discussion of MOGAs, we refer the reader to Coello (2000), Veldhuizen and Lamont (2000), Deb (2001) and Knowles and Corne (2005).”
• A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems (2016, background): “been particularly widely researched in the family of MOO algorithms [22], because they are capable of efficiently constructing an approximate PF.”
• Evolutionary computation and structural design: A survey of the state-of-the-art (2005, methodology): “Comprehensive surveys of various evolutionary multiobjective optimization methods, including detailed discussion on their strengths and weaknesses, can be found in [40,156,158,161,164,187].”
• Metaheuristics in combinatorial optimization: Overview and conceptual comparison (2003, background): “Recent successes were obtained in the rapidly growing bioinformatics area (see, e.g., Fogel et al. [2002]), but also in multi-objective optimization [Coello Coello 2000], and in evolvable hardware [Sipper et al. 1997].”
• Global Optimization Algorithms -- Theory and Application (2009, background): “Therefore, various forms of genetic algorithms [519] have been developed to.”
• Evolutionary many-objective optimisation: many once or one many? (2005, background): “The algorithm updates the current archive by identifying all the non-dominated solutions in the union of the old archive and current population.”
• ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems (2006, background): “The rise of EAs for multiobjective optimization in recent years is now well documented, with thorough reviews of the history and current state of the art available in [6], [9], and [16].”