Getting Started

Welcome to jMetalPy! This section will help you get up and running quickly.

Installation

Via pip:

$ pip install jmetalpy  # or "jmetalpy[distributed]"

Note

Alternative installation options:

$ pip install "jmetalpy[core]"        # Core components only
$ pip install "jmetalpy[docs]"        # Documentation building
$ pip install "jmetalpy[distributed]" # Parallel computing
$ pip install "jmetalpy[complete]"    # All dependencies

Via source code:

$ git clone https://github.com/jMetal/jMetalPy.git
$ python setup.py install

Quick Start

Here’s a simple example to get you started with jMetalPy:

from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.operator.selection import BinaryTournamentSelection
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations

# Define the problem
problem = ZDT1()

# Configure the algorithm
algorithm = NSGAII(
    problem=problem,
    population_size=100,
    offspring_population_size=100,
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
    crossover=SBXCrossover(probability=1.0, distribution_index=20),
    selection=BinaryTournamentSelection(),
    termination_criterion=StoppingByEvaluations(max_evaluations=25000)
)

# Run the algorithm
algorithm.run()
solutions = algorithm.get_result()

# Print results
print(f"Found {len(solutions)} solutions")

First Steps

What’s Next?

Once you’ve completed the quick start:

1. Explore the User Guide for comprehensive tutorials and examples

2. Browse the API Reference for detailed technical documentation

3. Check Advanced Topics for specialized use cases

4. Join the Community - contribute or ask questions

Key Concepts

Before diving deeper, familiarize yourself with these core concepts:

• Problems: Define what you want to optimize

• Algorithms: Methods to find optimal solutions

• Operators: Building blocks for algorithms (crossover, mutation, selection)

• Quality Indicators: Metrics to evaluate solution quality

• Experiments: Framework for systematic algorithm comparison