Evolutionary Algorithms for Solving Multi-Objective Problems

Coello Coello, C. A., Lamont, & Van Veldhuizen, D. A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems. Springer.

Type

Book

Intellectual base

Review or monograph

Year

2007

DOI registrant

Springer Nature

Link

https://doi.org/10.1007/978-0-387-36797-2 not authoritativeverified

Cited from

operations_research

Cited by

1 citation across 1 artifact.

Each citation links to the sentence it supports in the citing article.

Primes

• Multiobjective Optimization
  • The tooling ecosystem includes: specialized MOO libraries (pymoo in Python, PlatEMO in MATLAB, jMetal in Java, DEAP for evolutionary MOO); solver extensions in general-purpose optimization packages (AMPL, GAMS, JuMP, Pyomo all support MOO to varying degrees); visualization tools for Pareto frontiers (parallel coordinates, scatter plots, glyph plots for high-dimensional fronts); and substantial tutorial literature (Miettinen 1999, Deb 2001, Coello Coello, Lamont, and Van Veldhuizen (2007), and Branke et al. 2008 as canonical references).

    This sourceCanonical reference on evolutionary multiobjective optimization covering algorithms, tooling, performance assessment, and applications.

    Supported in partVerified against the publisher's abstract

    Abstract confirms a classroom-adapted textbook on evolutionary multiobjective algorithms; it says nothing about the libraries, solvers, or visualization tools listed.

    “This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom.”

    Read by an automated reader; not a human review. How support was checked

Verification

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