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EOS/docs/_generated/configoptimization.md
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14d8587c9c feat(genetic): make the fitness cache size configurable (#1376)
The per-run fitness cache of the GENETIC optimizer is unbounded. It
grows with individuals x generations (~110k keys at 300 x 400, about
1.1 kB each, 1.6 kB with an EV) and adds 120-170 MB to the peak memory
of a run, while only 6-15 % of the lookups hit. On 512 MB boards this
is the largest part of the optimizer's memory.

Add optimization.genetic.fitness_cache_max_entries:

- None (default): unbounded, the previous behaviour.
- 0: no cache; every genome is evaluated.
- N > 0: at most N keys. The cache is then an OrderedDict and evicts
  the oldest key first (FIFO, O(1)); LRU would cost bookkeeping on
  every hit for a cache that rarely hits.

A cache hit returns exactly what the deterministic evaluation would
return, so the plan for a fixed seed is identical with any setting.

Co-authored-by: Christin <christin@BigBrother.bikini.bottom.zone>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: Andreas <drbacke@gmx.de>
2026-10-11 09:19:33 +02:00

9.5 KiB

General Optimization Configuration

:::{table} optimization :widths: 10 20 10 5 5 30 :align: left

Name Environment Variable Type Read-Only Default Description
algorithm EOS_OPTIMIZATION__ALGORITHM <enum 'OptimizationAlgorithm'> rw required Optimization algorithm [GENETIC
algorithms list[str] ro N/A Available optimization algorithms.
genetic EOS_OPTIMIZATION__GENETIC GeneticCommonSettings rw required GENETIC optimization algorithm configuration.
genetic0 EOS_OPTIMIZATION__GENETIC0 Genetic0CommonSettings rw required GENETIC0 optimization algorithm configuration.
keys list[str] ro N/A The keys of the solution.
:::

Example Input

   {
       "optimization": {
           "algorithm": "GENETIC",
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "fitness_cache_max_entries": null,
               "measurement_max_age_seconds": 300,
               "tail_horizon_hours": 48,
               "terminal_value_mode": "AUTO",
               "terminal_value_euro_per_kwh": 0.0,
               "terminal_value_window_hours": 24,
               "penalties": {
                   "ev_soc_miss": 10
               }
           },
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               }
           }
       }
   }

Example Output

   {
       "optimization": {
           "algorithm": "GENETIC",
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "fitness_cache_max_entries": null,
               "measurement_max_age_seconds": 300,
               "tail_horizon_hours": 48,
               "terminal_value_mode": "AUTO",
               "terminal_value_euro_per_kwh": 0.0,
               "terminal_value_window_hours": 24,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "horizon": 24
           },
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "interval_sec": 3600,
               "horizon": 24
           },
           "algorithms": [
               "GENETIC",
               "GENETIC0"
           ],
           "keys": []
       }
   }

GENETIC0 Optimization Algorithm Configuration

:::{table} optimization::genetic0 :widths: 10 10 5 5 30 :align: left

Name Type Read-Only Default Description
generations Optional[int] rw 400 Number of generations to evolve [>= 10]. Defaults to 400.
horizon int ro N/A Number of optimization steps.
horizon_hours int rw 24 The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours.
individuals Optional[int] rw 300 Number of individuals (solutions) in the population [>= 10]. Defaults to 300.
interval_sec int ro N/A The optimization interval [sec]. Fixed to 1 hour (3600 seconds).
penalties dict[str, Union[float, int, str]] rw required Penalty parameters used in fitness evaluation.
seed Optional[int] rw None Random seed for reproducibility. None = random.
:::

Example Input

   {
       "optimization": {
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               }
           }
       }
   }

Example Output

   {
       "optimization": {
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "interval_sec": 3600,
               "horizon": 24
           }
       }
   }

GENETIC Optimization Algorithm Configuration

:::{table} optimization::genetic :widths: 10 10 5 5 30 :align: left

Name Type Read-Only Default Description
fitness_cache_max_entries Optional[int] rw None Maximum number of keys in the per-run fitness cache. None = unbounded (default), 0 = cache disabled, N > 0 = keep at most N keys and evict the oldest first. Every cached evaluation takes one or two keys. The cache only saves repeated evaluations; the result for a fixed seed is the same with any value. Limit it to cap memory on small devices.
generations Optional[int] rw 400 Number of generations to evolve [>= 10]. Defaults to 400.
horizon int ro N/A Number of optimization steps.
horizon_hours int rw 24 The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours.
individuals Optional[int] rw 300 Number of individuals (solutions) in the population [>= 10]. Defaults to 300.
interval_sec Literal[900, 3600] rw 3600 The optimization interval [sec]. Defaults to 3600 seconds (1 hour)
measurement_max_age_seconds int rw 300 Maximum age of SoC measurements for configuration-based optimization [s].
penalties dict[str, Union[float, int, str]] rw required Penalty parameters used in fitness evaluation.
seed Optional[int] rw None Random seed for reproducibility. None = random.
tail_horizon_hours int rw 48 Forecast lookahead after the control horizon [h]. No tail commands are issued. Set 0 to disable.
terminal_value_euro_per_kwh float rw 0.0 Value assigned to usable battery energy remaining at the end of the optimization horizon [EUR/kWh]. This terminal value is independent of the battery LCOS. Only used with terminal_value_mode = FIXED. Defaults to 0 EUR/kWh.
terminal_value_mode <enum 'TerminalValueMode'> rw AUTO How to value the energy left in the battery at the end of the control horizon. AUTO solves the forecast tail with an AUTO continuation proxy at its end (or only the proxy if tail is zero); FIXED uses 'terminal_value_euro_per_kwh'. Defaults to AUTO.
terminal_value_window_hours int rw 24 Length of the trailing window at the effective tail end the AUTO continuation curve is derived from [h]. One day covers a full load and PV cycle. Defaults to 24 hours.
:::

Example Input

   {
       "optimization": {
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "fitness_cache_max_entries": null,
               "measurement_max_age_seconds": 300,
               "tail_horizon_hours": 48,
               "terminal_value_mode": "AUTO",
               "terminal_value_euro_per_kwh": 0.0,
               "terminal_value_window_hours": 24,
               "penalties": {
                   "ev_soc_miss": 10
               }
           }
       }
   }

Example Output

   {
       "optimization": {
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "fitness_cache_max_entries": null,
               "measurement_max_age_seconds": 300,
               "tail_horizon_hours": 48,
               "terminal_value_mode": "AUTO",
               "terminal_value_euro_per_kwh": 0.0,
               "terminal_value_window_hours": 24,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "horizon": 24
           }
       }
   }