initial version of form to yaml conversion (WIP)

algorithms.yaml

@@ -1,50 +1,122 @@
-alg_nies:
-  can_evaluate_objectives_independently: null
-  code_examples: null
-  constraint_types:
-  - description: null
-    equality: null
-    hard: null
-    supports: null
-    type: box
-  description: A natural-gradient based evolutionary algorithm for integer-space
-    optimization
-  dynamics: null
-  fidelity_levels: null
-  implementations:
-  - impl_integeres
-  long_name: Natural Integer Evolutionary Strategies
-  modality_types:
-  - description: null
-    supports: default
-    type: unimodal
-  name: NIES
-  noise: null
-  number_variables:
-    max: 5000
-    min: 1
-  objectives: 1
-  partial_evaluation: null
+alg_mies:
+  type: algorithm
+  name: MIES
+  long_name: null
+  description: null
+  tags:
+    - noisy-evaluations
+    - multiple-fidelities
+    - dynamic-objective-functions
+    - Sequential
+    - partial-evaluations
+    - Evolutionary
+    - independent-objective-evaluations
+    - Population-based
+    - multimodal
+  references: null
+  implementations: null
+  objectives: null
   recommended_budget:
-    max: 10000000000
     min: 100
-  references: null
+    max: 10000000000
+  number_variables:
+    min: 1
+    max: 5000
+  variable_types:
+    -
+      type: integer
+      supports: default
+      description: null
+    -
+      type: binary
+      supports: default
+      description: null
+    -
+      type: continuous
+      supports: default
+      description: null
+    -
+      type: categorical
+      supports: default
+      description: null
+  constraint_types: []
+  dynamics: null
+  noise: null
+  partial_evaluation:
+    supports: no
+    description: Not supported
+  can_evaluate_objectives_independently:
+    supports: no
+    description: Not supported
+  modality_types: null
+  fidelity_levels: null
+  code_examples: null
   source: null
-  tags:
-  - Evolutionary
-  - Gradient-free
-  - Anytime
+alg_cma_es:
   type: algorithm
+  name: CMA-ES
+  long_name: null
+  description: "pycma is a Python implementation of CMA-ES and some related numerical optimization tools.\n\nThe CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is a randomized derivative-free numerical optimization algorithm for difficult (non-convex, ill-conditioned, multi-modal, rugged, noisy) optimization problems in continuous and mixed-integer search spaces. This package provides an implementation of the CMA-ES algorithm that includes the handling of\n* bound constraints via the 'bounds' = [lower, upper] option or the cma.BoundDomainTransform wrapper\n* linear and nonlinear constraints via the constraints argument to fmin2 or fmin_con2\n* noise via noise_handler=True as argument to fmin2\n* integer variables for mixed-integer problems via the 'integer_variables'=index_list option\n\nOther relevant information: There are plenty of other variants of CMA-ES"
+  tags:
+    - Parallel
+    - Anytime
+    - noisy-evaluations
+    - multiple-fidelities
+    - Gradient-free
+    - dynamic-objective-functions
+    - partial-evaluations
+    - Evolutionary
+    - independent-objective-evaluations
+    - Population-based
+    - Surrogate-based
+    - multimodal
+  references: null
+  implementations:
+    - impl_cma_es
+  objectives: null
+  recommended_budget:
+    min: 7
+    max: 10
+  number_variables:
+    min: 40
+    max: 40
   variable_types:
-  - description: null
-    supports: default
-    type: integer
-impl_integeres:
-  description: A Python implementation of NIES
-  language: python
-  links:
-  - type: source code
-    url: https://github.com/jacobdenobel/integer-es
-  name: impl_integeres
-  requirements: null
+    -
+      type: continuous
+      supports: default
+      description: null
+  constraint_types:
+    -
+      type: box
+      supports: conditional
+      hard: null
+      equality: null
+      description: null
+    -
+      type: linear
+      supports: conditional
+      hard: null
+      equality: null
+      description: null
+  dynamics: null
+  noise: null
+  partial_evaluation:
+    supports: no
+    description: Not supported
+  can_evaluate_objectives_independently:
+    supports: no
+    description: Not supported
+  modality_types: null
+  fidelity_levels: null
+  code_examples: null
+  source: null
+impl_cma_es:
   type: implementation
+  name: CMA-ES implementation
+  description: "pycma is a Python implementation of CMA-ES and some related numerical optimization tools.\n\nThe CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is a randomized derivative-free numerical optimization algorithm for difficult (non-convex, ill-conditioned, multi-modal, rugged, noisy) optimization problems in continuous and mixed-integer search spaces. This package provides an implementation of the CMA-ES algorithm that includes the handling of\n* bound constraints via the 'bounds' = [lower, upper] option or the cma.BoundDomainTransform wrapper\n* linear and nonlinear constraints via the constraints argument to fmin2 or fmin_con2\n* noise via noise_handler=True as argument to fmin2\n* integer variables for mixed-integer problems via the 'integer_variables'=index_list option\n\nOther relevant information: There are plenty of other variants of CMA-ES"
+  links:
+    -
+      type: source code
+      url: "https://github.com/CMA-ES/pycma"
+  language: Python
+  requirements: python

docs/algorithms.html

@@ -1,6 +1,6 @@
 <section class="table-shell">
     <div class="table-toolbar">
-        <details class="tag-toolbar-disclosure" open><summary class="toolbar-title">Filter By Tags</summary><div class="tag-toolbar-actions"><button type="button" class="toolbar-btn" id="clear-tag-filter">Clear tag filter</button><span class="tag-filter-status" id="active-tag-filter">No active tag filter</span></div><div class="tag-groups"><section class="tag-group"><h4 class="tag-group-title">Performance</h4><div class="tag-group-chips"><button type="button" class="tag-filter-chip" data-tag="Anytime">Anytime</button></div></section><section class="tag-group"><h4 class="tag-group-title">Methodology</h4><div class="tag-group-chips"><button type="button" class="tag-filter-chip" data-tag="Gradient-free">Gradient-free</button></div></section><section class="tag-group"><h4 class="tag-group-title">Families</h4><div class="tag-group-chips"><button type="button" class="tag-filter-chip" data-tag="Evolutionary">Evolutionary</button></div></section></div></details>
+        <details class="tag-toolbar-disclosure" open><summary class="toolbar-title">Filter By Tags</summary><div class="tag-toolbar-actions"><button type="button" class="toolbar-btn" id="clear-tag-filter">Clear tag filter</button><span class="tag-filter-status" id="active-tag-filter">No active tag filter</span></div><div class="tag-groups"><section class="tag-group"><h4 class="tag-group-title">Performance</h4><div class="tag-group-chips"><button type="button" class="tag-filter-chip" data-tag="Anytime">Anytime</button></div></section><section class="tag-group"><h4 class="tag-group-title">Execution</h4><div class="tag-group-chips"><button type="button" class="tag-filter-chip" data-tag="Parallel">Parallel</button><button type="button" class="tag-filter-chip" data-tag="Sequential">Sequential</button></div></section><section class="tag-group"><h4 class="tag-group-title">Methodology</h4><div class="tag-group-chips"><button type="button" class="tag-filter-chip" data-tag="Gradient-free">Gradient-free</button><button type="button" class="tag-filter-chip" data-tag="Population-based">Population-based</button></div></section><section class="tag-group"><h4 class="tag-group-title">Families</h4><div class="tag-group-chips"><button type="button" class="tag-filter-chip" data-tag="Evolutionary">Evolutionary</button></div></section><section class="tag-group"><h4 class="tag-group-title">Other</h4><div class="tag-group-chips"><button type="button" class="tag-filter-chip" data-tag="dynamic-objective-functions">dynamic-objective-functions</button><button type="button" class="tag-filter-chip" data-tag="independent-objective-evaluations">independent-objective-evaluations</button><button type="button" class="tag-filter-chip" data-tag="multimodal">multimodal</button><button type="button" class="tag-filter-chip" data-tag="multiple-fidelities">multiple-fidelities</button><button type="button" class="tag-filter-chip" data-tag="noisy-evaluations">noisy-evaluations</button><button type="button" class="tag-filter-chip" data-tag="partial-evaluations">partial-evaluations</button><button type="button" class="tag-filter-chip" data-tag="Surrogate-based">Surrogate-based</button></div></section></div></details>
         <details class="column-toolbar-disclosure">
             <summary class="toolbar-title">Toggle Visible Columns</summary>
             <div class="toolbar-actions">
@@ -40,24 +40,46 @@
   </thead>
   <tbody>
     <tr>
-      <td>alg_nies</td>
-      <td>NIES</td>
-      <td>1</td>
-      <td>integer</td>
+      <td>alg_mies</td>
+      <td>MIES</td>
+      <td></td>
+      <td>binary | integer | continuous | categorical</td>
       <td>1–5000</td>
-      <td>box</td>
       <td></td>
       <td></td>
-      <td>unimodal</td>
       <td></td>
       <td></td>
+      <td>no</td>
+      <td>no</td>
       <td>100–10000000000</td>
       <td></td>
-      <td>Natural Integer Evolutionary Strategies</td>
-      <td>A natural-gradient based evolutionary algorithm for integer-space optimization</td>
-      <td><button type="button" class="table-tag" data-tag="Anytime">Anytime</button> <button type="button" class="table-tag" data-tag="Evolutionary">Evolutionary</button> <button type="button" class="table-tag" data-tag="Gradient-free">Gradient-free</button></td>
       <td></td>
-      <td>impl_integeres</td>
+      <td></td>
+      <td><button type="button" class="table-tag" data-tag="dynamic-objective-functions">dynamic-objective-functions</button> <button type="button" class="table-tag" data-tag="Evolutionary">Evolutionary</button> <button type="button" class="table-tag" data-tag="independent-objective-evaluations">independent-objective-evaluations</button> <button type="button" class="table-tag" data-tag="multimodal">multimodal</button> <button type="button" class="table-tag" data-tag="multiple-fidelities">multiple-fidelities</button> <button type="button" class="table-tag" data-tag="noisy-evaluations">noisy-evaluations</button> <button type="button" class="table-tag" data-tag="partial-evaluations">partial-evaluations</button> <button type="button" class="table-tag" data-tag="Population-based">Population-based</button> <button type="button" class="table-tag" data-tag="Sequential">Sequential</button></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr>
+      <td>alg_cma_es</td>
+      <td>CMA-ES</td>
+      <td></td>
+      <td>continuous</td>
+      <td>40</td>
+      <td>box | linear</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td>no</td>
+      <td>7–10</td>
+      <td></td>
+      <td></td>
+      <td>pycma is a Python implementation of CMA-ES and some related numerical optimization tools.\n\nThe CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is a randomized derivative-free numerical optimization algorithm for difficult (non-convex, ill-conditioned, multi-modal, rugged, noisy) optimization problems in continuous and mixed-integer search spaces. This package provides an implementation of the CMA-ES algorithm that includes the handling of\n* bound constraints via the 'bounds' = [lower, upper] option or the cma.BoundDomainTransform wrapper\n* linear and nonlinear constraints via the constraints argument to fmin2 or fmin_con2\n* noise via noise_handler=True as argument to fmin2\n* integer variables for mixed-integer problems via the 'integer_variables'=index_list option\n\nOther relevant information: There are plenty of other variants of CMA-ES</td>
+      <td><button type="button" class="table-tag" data-tag="Anytime">Anytime</button> <button type="button" class="table-tag" data-tag="dynamic-objective-functions">dynamic-objective-functions</button> <button type="button" class="table-tag" data-tag="Evolutionary">Evolutionary</button> <button type="button" class="table-tag" data-tag="Gradient-free">Gradient-free</button> <button type="button" class="table-tag" data-tag="independent-objective-evaluations">independent-objective-evaluations</button> <button type="button" class="table-tag" data-tag="multimodal">multimodal</button> <button type="button" class="table-tag" data-tag="multiple-fidelities">multiple-fidelities</button> <button type="button" class="table-tag" data-tag="noisy-evaluations">noisy-evaluations</button> <button type="button" class="table-tag" data-tag="Parallel">Parallel</button> <button type="button" class="table-tag" data-tag="partial-evaluations">partial-evaluations</button> <button type="button" class="table-tag" data-tag="Population-based">Population-based</button> <button type="button" class="table-tag" data-tag="Surrogate-based">Surrogate-based</button></td>
+      <td></td>
+      <td>CMA-ES implementation</td>
       <td></td>
       <td></td>
     </tr>