EvoX v1.1.1 Release Note
What's ChangedThis minor release primarily includes bug fixes and improveme
MetaDE: Evolving Differential Evolution by Differential Evolution
Differential Evolution (DE), one of the core algorithms in evolutionary
EvoX 1.1 Release: Now with torch.compile (TorchDynamo)
We are excited to announce the release of EvoX 1.1, introducing full integration
<< Key Features >>
🚀 Ultra Performance
● Supports acceleration on heterogeneous hardware, including both CPUs and GPUs, achieving over100x speedups.
● Integrated distributed workflows that scale seamlessly across multiple nodes or devices.
🌐 All-in-One Solution
● Includes 50+ algorithms for a wide range of use cases, fully supporting single- and multi-objective optimization.
● Provides a hierarchical architecture for complex tasks such as meta learning, hyperparameter optimization, and neuroevolution.
🛠️ Easy-to-Use Design
● Fully compatible with EvoX and its ecosystem, simplifying algorithmic development with a tailored programming model.
● Ensures effortless setup with one-click installation for Windows users.
📚 Extensive Benchmark Suites
● Features 100+ benchmark problems spanning single-objective optimization, multi-objective optimization, and real-world engineering challenges.
🎮 Support for Physics Engines
● Integrates seamlessly with physics engines like Brax and other popular frameworks for reinforcement learning.
⚙️ Customizable Problems
● Provides an encapsulated module for defining and evaluating custom problems tailored to user needs, with seamless integration into real-world applications and datasets.
🔍 Ready-to-Use Tools
● Offers a comprehensive set of visualization tools for analyzing evolutionary processes across various tasks.
🛠️ Customizable Modules
● Enables users to integrate their own visualization code, allowing for tailored and flexible visualizations.
📂 Real-Time Data Streaming
● Leverages the tailored .exv format to simplify and accelerate real-time data streaming.
Single-objective Optimization
import torch
from evox.algorithms.pso_variants import PSO
from evox.problems.numerical import Ackley
from evox.workflows import StdWorkflow, EvalMonitor
torch.set_default_device("cuda")
# Define the algorithm
algorithm = PSO(pop_size=100, lb=-32 * torch.ones(10), ub=32 * torch.ones(10))
problem = Ackley()
monitor = EvalMonitor()
workflow = StdWorkflow(algorithm, problem, monitor)
workflow.init_step()
for i in range(100):
workflow.step()
monitor.plot()
Multi-objective Optimization
import torch
from evox.algorithms import RVEA
from evox.problems.numerical import DTLZ2
from evox.workflows import StdWorkflow, EvalMonitor
torch.set_default_device("cuda")
prob = DTLZ2(m=3)
pf = prob.pf()
algo = RVEA(pop_size=100, n_objs=3, lb=-torch.zeros(12), ub=torch.ones(12))
monitor = EvalMonitor()
workflow = StdWorkflow(algo, prob, monitor)
workflow.init_step()
for i in range(100):
workflow.step()
Neuroevolution
import torch
import torch.nn as nn
from evox.algorithms import PSO
from evox.problems.neuroevolution.brax import BraxProblem
from evox.utils import ParamsAndVector
from evox.workflows import EvalMonitor, StdWorkflow
class SimpleMLP(nn.Module):
def __init__(self):
super().__init__()
# observation space is 17-dim, action space is 6-dim.
self.features = nn.Sequential(nn.Linear(17, 8), nn.Tanh(), nn.Linear(8, 6))
def forward(self, x):
x = self.features(x)
return torch.tanh(x)
# Initialize the MLP model
torch.set_default_device('cuda')
model = SimpleMLP()
adapter = ParamsAndVector(dummy_model=model)
# Set the population size
POP_SIZE = 1024
# Get the bound of the PSO algorithm
model_params = dict(model.named_parameters())
pop_center = adapter.to_vector(model_params)
lb = torch.full_like(pop_center, -5)
ub = torch.full_like(pop_center, 5)
# Initialize the PSO, and you can also use any other algorithms
algorithm = PSO(pop_size=POP_SIZE, lb=lb, ub=ub)
# Initialize the Brax problem
problem = BraxProblem(
policy=model,
env_name="halfcheetah",
max_episode_length=1000,
num_episodes=3,
pop_size=POP_SIZE,
)
# set an monitor, and it can record the top 3 best fitnesses
monitor = EvalMonitor(topk=3)
# Initiate an workflow
workflow = StdWorkflow(
algorithm=algorithm,
problem=problem,
monitor=monitor,
opt_direction="max",
solution_transform=adapter,
)
workflow.init_step()
for i in range(50):
workflow.step() Explore a rich ecosystem of libraries, tools, and more to support development.
Community
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