ecapy 1.0.1

Evolutionary Centers Algorithm: Module for Python coded in C

# Evolutionary Centers Algorithm

ECA is a physics-inspired algorithm based on the center of mass concept on
a D-dimensional space for real-parameter single-objective optimization. The
general idea is to promote the creation of an irregular body using K mass points
in the current population, then the center of mass is calculated to get a new direction
for the next population... [read more.](https://www.dropbox.com/s/kqc22ki2edjtt0y/ECA-optimization.pdf)

## Parameters
- Parameters (suggested):
- Objective function: `fobj`
- Dimension: `D`
- K-value:
`K = 7`
- Population size:
`N = K*D`
- stepsize:
`eta_max = 2.0`
- binomial probability:
`P_bin = 0.03`
- Exploit parameter:
`P_exploit = 0.95`
- Max. number of evaluations:
`max_evals = 10000*D`

- Bounds:
- Lower: `low_bound`
- Upper: `up_bound`

- Search Type:
- Maximize:
- `minimize = True`
- minimize:
- `minimize = False`


## Example

You can write Python code to use ECA in your project:

```python
from ecapy import eca

# D-dimensional sphere function
def sphere(x):
s = 0.0
for xi in x:
s += xi**2
return s

x, fx = eca(sphere, D = 10, minimize=True)

```