"""
Genetic Algorithm (GA)
This module implements the Genetic Algorithm for single-objective optimization problems.
References
----------
[1] David E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, 1989.
[2] John H. Holland. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor, MI, 1st edition, 1975. Reprinted by MIT Press in 1992.
Notes
-----
Author: Jiangtao Shen
Email: j.shen5@exeter.ac.uk
Date: 2025.11.11
Version: 1.0
"""
from tqdm import tqdm
import time
import numpy as np
from ddmtolab.Methods.Algo_Methods.algo_utils import *
[docs]
class GA:
"""
Genetic Algorithm for single-objective optimization.
Attributes
----------
algorithm_information : dict
Dictionary containing algorithm capabilities and requirements
"""
algorithm_information = {
'n_tasks': '[1, K]',
'dims': 'unequal',
'objs': 'equal',
'n_objs': '1',
'cons': 'unequal',
'n_cons': '[0, C]',
'expensive': 'False',
'knowledge_transfer': 'False',
'n': 'unequal',
'max_nfes': 'unequal'
}
@classmethod
def get_algorithm_information(cls, print_info=True):
return get_algorithm_information(cls, print_info)
[docs]
def __init__(self, problem, n=None, max_nfes=None, muc=2.0, mum=5.0, save_data=True, save_path='./Data',
name='GA', disable_tqdm=True):
"""
Initialize Genetic Algorithm.
Parameters
----------
problem : MTOP
Multi-task optimization problem instance
n : int or List[int], optional
Population size per task (default: 100)
max_nfes : int or List[int], optional
Maximum number of function evaluations per task (default: 10000)
muc : float, optional
Distribution index for simulated binary crossover (SBX) (default: 2.0)
mum : float, optional
Distribution index for polynomial mutation (PM) (default: 5.0)
save_data : bool, optional
Whether to save optimization data (default: True)
save_path : str, optional
Path to save results (default: './TestData')
name : str, optional
Name for the experiment (default: 'GA_test')
disable_tqdm : bool, optional
Whether to disable progress bar (default: True)
"""
self.problem = problem
self.n = n if n is not None else 100
self.max_nfes = max_nfes if max_nfes is not None else 10000
self.muc = muc
self.mum = mum
self.save_data = save_data
self.save_path = save_path
self.name = name
self.disable_tqdm = disable_tqdm
[docs]
def optimize(self):
"""
Execute the Genetic Algorithm.
Returns
-------
Results
Optimization results containing decision variables, objectives, and runtime
"""
start_time = time.time()
problem = self.problem
nt = problem.n_tasks
n_per_task = par_list(self.n, nt)
max_nfes_per_task = par_list(self.max_nfes, nt)
# Initialize population in [0,1] space and evaluate for each task
decs = initialization(problem, n_per_task)
objs, cons = evaluation(problem, decs)
nfes_per_task = n_per_task.copy()
all_decs, all_objs, all_cons = init_history(decs, objs, cons)
total_nfes = sum(max_nfes_per_task)
pbar = tqdm(total=total_nfes, initial=sum(n_per_task), desc=f"{self.name}",
disable=self.disable_tqdm)
while sum(nfes_per_task) < total_nfes:
# Skip tasks that have exhausted their evaluation budget
active_tasks = [i for i in range(nt) if nfes_per_task[i] < max_nfes_per_task[i]]
if not active_tasks:
break
for i in active_tasks:
# Generate offspring through crossover and mutation
off_decs = ga_generation(decs[i], self.muc, self.mum)
off_objs, off_cons = evaluation_single(problem, off_decs, i)
# Merge parent and offspring populations
objs[i], decs[i], cons[i] = vstack_groups(
(objs[i], off_objs),
(decs[i], off_decs),
(cons[i], off_cons)
)
# Calculate constraint violations
cvs = np.sum(np.maximum(0, cons[i]), axis=1)
# Selection based on constraint violation first, then objective
# Sort by constraint violation (ascending), then by objective (ascending)
sort_indices = np.lexsort((objs[i].flatten(), cvs))
# Select top n_per_task[i] individuals
index = sort_indices[:n_per_task[i]]
objs[i], decs[i], cons[i] = select_by_index(index,objs[i], decs[i], cons[i])
nfes_per_task[i] += n_per_task[i]
pbar.update(n_per_task[i])
# Append current population to history
append_history(all_decs[i], decs[i], all_objs[i], objs[i], all_cons[i], cons[i])
pbar.close()
runtime = time.time() - start_time
# Save results
results = build_save_results(all_decs=all_decs, all_objs=all_objs, runtime=runtime, max_nfes=nfes_per_task,
all_cons=all_cons, bounds=problem.bounds, save_path=self.save_path,
filename=self.name, save_data=self.save_data)
return results