Release Date 29 January 2024
1. Solve bugs when multi-objective optimization is used. #238
2. When the `stop_ciiteria` parameter is used with the `reach` keyword, then multiple numeric values can be passed when solving a multi-objective problem. For example, if a problem has 3 objective functions, then `stop_criteria="reach_10_20_30"` means the GA stops if the fitness of the 3 objectives are at least 10, 20, and 30, respectively. The number values must match the number of objective functions. If a single value found (e.g. `stop_criteria=reach_5`) when solving a multi-objective problem, then it is used across all the objectives. #238
3. The `delay_after_gen` parameter is now deprecated and will be removed in a future release. If it is necessary to have a time delay after each generation, then assign a callback function/method to the `on_generation` parameter to pause the evolution.
4. Parallel processing now supports calculating the fitness during adaptive mutation. #201
5. The population size can be changed during runtime by changing all the parameters that would affect the size of any thing used by the GA. For more information, check the [Change Population Size during Runtime](https://pygad.readthedocs.io/en/latest/pygad_more.html#change-population-size-during-runtime) section. #234
6. When a dictionary exists in the `gene_space` parameter without a step, then mutation occurs by adding a random value to the gene value. The random vaue is generated based on the 2 parameters `random_mutation_min_val` and `random_mutation_max_val`. For more information, check the [How Mutation Works with the gene_space Parameter?](https://pygad.readthedocs.io/en/latest/pygad_more.html#how-mutation-works-with-the-gene-space-parameter) section. #229
7. Add `object` as a supported data type for int (GA.supported_int_types) and float (GA.supported_float_types). #174
8. Use the `raise` clause instead of the `sys.exit(-1)` to terminate the execution. #213
9. Fix a bug when multi-objective optimization is used with batch fitness calculation (e.g. `fitness_batch_size` set to a non-zero number).
10. Fix a bug in the `pygad.py` script when finding the index of the best solution. It does not work properly with multi-objective optimization where `self.best_solutions_fitness` have multiple columns.