hmep alternatives and similar packages
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8.4 0.0 hmep VS mooGenetic algorithm library for Haskell. Binary and continuous (real-coded) GAs. Binary GAs: binary and Gray encoding; point mutation; one-point, two-point, and uniform crossover. Continuous GAs: Gaussian mutation; BLX-α, UNDX, and SBX crossover. Selection operators: roulette, tournament, and stochastic universal sampling (SUS); with optional niching, ranking, and scaling. Replacement strategies: generational with elitism and steady state. Constrained optimization: random constrained initialization, death penalty, constrained selection without a penalty function. Multi-objective optimization: NSGA-II and constrained NSGA-II.
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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Multi Expression Programming
You say, not enough Haskell machine learning libraries?
Here is yet another one!
There exist many other Genetic Algorithm (GA) Haskell packages. Personally I have used simple genetic algorithm, GA, and moo for quite a long time. The last package was the most preferred, but the other two are also great.
However, when I came up with this MEP paper, to my surprise there was no MEP implementation in Haskell. Soon I realized that existing GA packages are limited, and it would be more efficient to implement MEP from scratch.
Multi Expression Programming is a genetic programming variant encoding multiple solutions in the same chromosome. A chromosome is a computer program. Each gene is featuring code reuse.
How MEP is different from other genetic programming (GP) methods?
Consider a classical example of tree-based GP.
The number of nodes to encode
using a binary tree is
With MEP encoding, however, redundancies can be dramatically
diminished so that the
that encodes the same expression has only
That often results in significantly reduced computational costs
when evaluating MEP chromosomes. Moreover, all the intermediate
solutions such as
x^(N/4), etc. are provided by the
chromosome as well.
For more details, please check http://mepx.org/papers.html and https://en.wikipedia.org/wiki/Multi_expression_programming.
MEP in open source
- Works out of the box. You may use one of the elaborated examples to quickly tailor to your needs.
- Flexibility. The
hmeppackage provides adjustable and composable building blocks such as selection, mutation and crossover operators. One is also free to use their own operators.
hmepcan be applied to solve regression problems with one or multiple outputs. It means, you can approximate unknown functions or solve classification tasks. The only requirement is a custom loss function.
$ git clone https://github.com/masterdezign/hmep.git && cd hmep $ stack build --install-ghc
A CLI interface to Haskell multi expression programming Usage: hmep -f <input file> [-l|--length 30] [-m|--mutation 0.05] [-r|--var 0.1] [-c|--const 0.05] [-p|--population 200] [-t|--total 200] Available options: -h,--help Show this help text -f <input file> Input file path. Format: comma-separated, two columns. -l,--length 30 Chromosome length -m,--mutation 0.05 Mutation probability -r,--var 0.1 Probability to generate a new variable gene -c,--const 0.05 Probability to generate a new constant gene -p,--population 200 Population size -t,--total 200 Total number of iterations
Example: run for total of 200 algorithm iterations
$ stack exec hmep -- -f data/sine.txt -t 200
Chromosome length: 30 Population size: 200 Mutation probability: 5.0e-2 Probability to generate a new variable gene: 0.1 Probability to generate a new constant gene: 5.0e-2 Probability to generate a new operator: 0.85 Reading file data/sine.txt Fetched 50 records Average loss in the initial population 0.6164572493880963 Population 5: average loss 0.36179141986463337 Population 10: average loss 0.35977590095295237 Population 15: average loss 0.3592976870934518 Population 20: average loss 0.35839623098861284 Population 25: average loss 0.35424451881439295 Population 30: average loss 0.31573374522629394 Population 35: average loss 0.1864152668405434 Population 40: average loss 8.966643495391169e-2 Population 45: average loss 8.522968243289145e-2 Population 50: average loss 8.522968243289145e-2 ... Population 200: average loss 5.51041829148264e-2 Interpreted expression: v2 = x0 * x0 v4 = -0.12453785273085771 * x0 v5 = -0.12453785273085771 + v4 v6 = x0 * v4 v7 = v4 * -0.12453785273085771 v8 = v4 * v2 v12 = x0 + v5 v14 = v8 + x0 v19 = v8 * v7 v22 = v19 * v6 v23 = v22 * v12 result = v14 + v23
CLI application source is [here](app/CLI/Main.hs).
Library Example 1
Now that the package is built, run the first demo to
$ stack exec hmep-demo Average loss in the initial population 15.268705681244962 Population 10: average loss 14.709728527360586 Population 20: average loss 13.497114190675477 Population 30: average loss 8.953185872653737 Population 40: average loss 8.953185872653737 Population 50: average loss 3.3219954564955856e-15 Interpreted expression: v1 = sin x0 v2 = v1 * v1 result = 1 - v2
Effectively, the solution
cos^2(x) = 1 - sin^2(x) was found.
Of course, MEP is a stochastic method, meaning that there is
no guarantee to find the globally optimal solution.
The unknown function approximation problem can be illustrated
by the following suboptimal solution for a given set of random
data points (blue crosses). This example was produced by another run of
the [same demo](app/Demo1/Main.hs), after 100 generations of 100 chromosomes
in each. The following expression was obtained
y(x) = 3*0.31248786462471034 - sin(sin^2(x)).
Interestingly, the approximating function lies symmetrically
in-between the extrema of the unknown function, approximately
described by the blue crosses.
Library Example 2
A similar example is to approximate
sin(x) using only
addition and multiplication operators, i.e. with polynomials.
$ stack exec hmep-sin-approximation
The algorithm is able to automatically figure out the
x. That is where MEP really shines. We [calculate](app/Demo2/Main.hs)
c'length = 30 expressions represented by each chromosome gene practically with no
additional computational penalty. We choose the best expression among those 30
in each chromosome of the population
c'popSize = 200.
In this run, we have automatically obtained a
seventh degree polynomial
coded by 14 genes. Pretty cool, huh?
v1 = -5.936286355387799e-2 + -5.936286355387799e-2 v4 = x0 + x0 v5 = v1 * x0 v7 = v4 * x0 v8 = v1 * v5 v9 = x0 * x0 v10 = v8 * v9 v11 = x0 * v10 v15 = -5.936286355387799e-2 * x0 v18 = v10 * v11 v20 = v7 * v15 v21 = v15 + x0 v25 = v21 + v20 result = v18 + v25
0.940637136446122*x - 0.118725727107756*x**3 + 0.000198691529073357*x**7,
can be regarded as a hand-tuned version of
x - x^3/3! + x^7/7! (the analytic expression
x - x^3/3! + x^5/5! - x^7/7!).
That is impressive given that this is computed in fourteen steps!
Interestingly, we also observe that roughly half of expressions remain unused (e.g. v2, v3, v12...).
The result of approximation is [visualized](doc/sin_approx.py) below:
From the log below, one can also infer that obtained approximation is better than analytical Taylor sine expansions of 3rd and 5th orders. And naturally, is worse than the 7th order Taylor expansion:
MEP expression: Average distance for 300 points: 0.0303 3rd-order Taylor sine expansion: Average distance for 300 points: 0.3633 5rd-order Taylor sine expansion: Average distance for 300 points: 0.0688 7rd-order Taylor sine expansion: Average distance for 300 points: 0.0079
This library is written and maintained by Bogdan Penkovsky