Release v9.0.0 · jenetics/jenetics
Improvements
Update Java 25 and optimize code for new Java version.
#917: ScopedValue for RandomRegistry class.
#940: Remove deprecated API.
#955: Make IntStream counting more robust.
Release v8.3.0 · jenetics/jenetics
Improvements
#933: Deprecate RandomAdapter for removal.
#935: Compile and test Jenetics with Java 24/25
#938: Convert Range classes into records.
#943: Remove `org.apache.commons:commons-math3´ te...
Release v8.2.0 · jenetics/jenetics
Improvements
#889: Allow adding annotations to Cfg elements for Grammatical Evolution.
final var cfg2 = Cfg.<String>builder()
.R("expr", rule -> rule
.N("num", "annotation 1")
...
Release v8.1.0 · jenetics/jenetics
Improvements
#822: Improve the build script for generating combined Javadoc.
#898: Add support for reading data from CSV files or strings. This simplifies the code for regression problems.
static...
#7AYW #Day3 #MultiobjectiveOptimizationIn collaboration with an international chemicals manufacturer, Matteo Cosmi shows a linear programming model to optimize a deterministic multi-objective supply-chain problem aimed at minimizing CO2 emissions and their related costs.
https://t.co/KkmpRVVUXC 
AIROyoung on Twitter
“#7AYW #Day3 #MultiobjectiveOptimization
In collaboration with an international chemicals manufacturer, Matteo Cosmi shows a linear programming model to optimize a deterministic multi-objective supply-chain problem aimed at minimizing CO2 emissions and their related costs.”
#7AYW #Day3 #MultiobjectiveOptimizationIn the context of home healthcare, Valentina Bonomi presents a lexicographic approach to solve a Nurse Routing Problem focused on fairness, with conflicting objective functions for patients, caregivers and the hospital.
https://t.co/TbXE03q9Zp 
AIROyoung on Twitter
“#7AYW #Day3 #MultiobjectiveOptimization
In the context of home healthcare, Valentina Bonomi presents a lexicographic approach to solve a Nurse Routing Problem focused on fairness, with conflicting objective functions for patients, caregivers and the hospital.”
#preprint on a multi-swarm approach for multi-objective optimization out now! Scheme is based on Consensus-based optimization / sampling
#CBO #MultiobjectiveOptimization See
http://arxiv.org/abs/2211.15737 
Consensus-Based Optimization for Multi-Objective Problems: A Multi-Swarm Approach
We propose a multi-swarm approach to approximate the Pareto front of general
multi-objective optimization problems that is based on the Consensus-based
Optimization method (CBO). The algorithm is motivated step by step beginning
with a simple extension of CBO based on fixed scalarization weights. To
overcome the issue of choosing the weights we propose an adaptive weight
strategy in the second modelling step. The modelling process is concluded with
the incorporation of a penalty strategy that avoids clusters along the Pareto
front and a diffusion term that prevents collapsing swarms. Altogether the
proposed $K$-swarm CBO algorithm is tailored for a diverse approximation of the
Pareto front and, simultaneously, the efficient set of general non-convex
multi-objective problems. The feasibility of the approach is justified by
analytic results, including convergence proofs, and a performance comparison to
the well-known non-dominated sorting genetic algorithm (NSGA2) and the recently
proposed one-swarm approach for multi-objective problems involving
Consensus-based Optimization.
Jenetics GA
AIROYoung
Claudia Totzeck